ORIGINAL_ARTICLE
Optimal Scheduling of Battery Energy Storage System in Distribution Network Considering Uncertainties using hybrid Monte Carlo- Genetic Approach
This paper proposes a novel hybrid Monte Carlo simulation-genetic approach (MCS-GA) for optimal operation of a distribution network considering renewable energy generation systems (REGSs) and battery energy storage systems (BESSs). The aim of this paper is to design an optimal charging /discharging scheduling of BESSs so that the total daily profit of distribution company (Disco) can be maximized. In this study, the power generation of REGSs such as photovoltaic resources (PVs) and the network electricity prices are studied through their uncertainty natures. The probability distribution function (PDF), is used to account for uncertainties in this paper. Also, the Monte Carlo simulation (MCS) is applied to generate different scenarios of network electricity prices and solar irradiation of PVs. Optimal scheduling of BESSs can be performed by genetic algorithm (GA). In this paper, firstly, the charging and discharging state of BESSs (positive or negative sign of battery power) is determined according to the variable amount of the electricity prices and power produced from PVs, which have been obtained from the Monte Carlo simulation. Then by using the GA, optimal amount of BESSs is determined. Therefore, a hybrid MCS-GA is used to solve this problem. Numerical examples are presented to illustrate the optimal charging/discharging power of the battery for maximizing the total daily profit.
http://joape.uma.ac.ir/article_632_02d445095ef5de28bcf76f5fee404d52.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
1
12
10.22098/joape.2017.3385.1271
Battery Energy Storage Systems
Optimal Operation
Uncertainty Modeling
Monte Carlo simulation
genetic algorithm
R.
Afshan
true
1
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
AUTHOR
J.
Salehi
j.salehi@azaruniv.ac.ir
true
2
Azarbaijan Shahid Madani University
Azarbaijan Shahid Madani University
Azarbaijan Shahid Madani University
LEAD_AUTHOR
[1] N. Rugthaicharoencheep and S. Auchariyamet, “Technical and economic impacts of distributed generation on distribution system”, Int. J. Electr. Comput. Energetic Electron. Commun. Eng., vol. 6, no. 4, pp.385-389,2012.
1
[2] I. Dincer, “Renewable energy and sustainable development: a crucial review”, Renewable Sustainable Energy Rev., vol. 4, no. 2, pp. 157-175, 2000.
2
[3] F. Shaaban, M. and E.F. El-Saadany “Optimal allocation of renewable DG for reliability improvement and losses reduction” in Proce. of Power Energy Soc. Gen. Meeting, San Diego, CA, USA, 2012, pp.1-8.
3
[4] S. Surender Reddy and P.R. Bijwe, “Real time economic dispatch considering renewable energy resources”, Renewable Energy, vol. 83, pp. 1215-1226, 2015.
4
[5] E. Mohammadi, and S. Esmaeili, “A Novel optimal placement of PV system for loss reduction and voltage profile improvement”, Tech. Phys. Prob. Eng., vol.4, no. 4, pp. 11-16, 2012.
5
[6] B. Zeng, J. Zhang, Xu. Yang, J. Wang, J. Dong, and Yu. Zhang, “Integrated planning for transition to low-carbon distribution system with renewable energy generation and demand response”, IEEE Trans. power syst., vol. 29, no. 3, pp.1153-1165,2014.
6
[7] Y.M. Atwa, E.F. El-Saadany, and M.M.A. Salama, “Optimal renewable resources mix for distribution system energy loss minimization”, IEEE Trans. Power Syst., vol. 25, no. 1, pp. 360-370, 2010.
7
[8] S. Talari, M. Yazdaninejad, and M.-R. Haghifam, “Stochastic-based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads”, IET Gen., Transm. Distrib., vol. 9, pp. 1498-1509, 2015.
8
[9] A. Badri, K. Hoseinpour Lonbar, “Stochastic multiperiod decision making framework of an electricity retailer considering aggregated optimal charging and discharging of electric vehicles”, J. Oper. Autom. Power Eng., vol. 3, no. 1, pp. 34-46, 2015.
9
[10] S.M. Mohseni-Bonab, A. Rabiee, S. Jalilzadeh, B. Mohammadi-Ivatloo, S. Nojavan, “Probabilistic multi objective optimal reactive power dispatch considering load uncertainties using Monte Carlo simulations”, J. Oper. Autom. Power Eng., vol. 3, no. 1, pp. 83-93, 2015.
10
[11] N. Nikmehr, and S. Najafi-Ravadanegh, “Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm”, IET Renewable Power Gen., vol. 9, pp. 982-990,2015.
11
[12] A. Zakariazadeh, S. Jadid, and P. Siano, "Stochastic operational scheduling of smart distribution system considering wind generation and demand response programs", Int. J. Electr. Power Energy Syst., vol. 63, pp. 218-225,2014.
12
[13] M. Haghifam, H. Falaghi, and O. Malik, “Risk-based distributed generation placement”, IET Gen. Transm. Distrib., vol. 2, pp. 252-260, 2008.
13
[14] A. Soroudi, “Possibilistic-scenario model for DG impact assessment on distribution networks in an uncertain environment”, IEEE Trans. Power Syst., vol. 27, no. 3, pp.1283-1293, 2012.
14
[15] J. Eyer, G. Corey, “Energy storage for the electricity grid: Benefits and market potential assessment guide”, Sandia National Laboratories, 2010, pp. 380.
15
[16] B. Berseneff, et al., “The significance of energy storage for renewable energy generation and the role of instrumentation and measurement”, in IEEE Instrum. Meas. Magazine, pp.32-40, 2014.
16
[17] T. Zhang, “The economic benefits of battery energy storage system in electric distribution system”, Worcester Polytechnic Institute, pp.324, 2013.
17
[18] B. Lu, M. Shahidehpour, “Short-term scheduling of battery in grid-connected PV/battery system”, IEEE Trans. Power Syst., vol. 20, no.2, pp.1053-1061, 2005.
18
[19] J.-H. Teng, et al. “Optimal charging/discharging scheduling of battery storage systems for distribution systems interconnected with sizeable PV generation systems”, IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1425-1433, 2013.
19
[20] C.A. Hill, M.C. Such, Do. Chen, J. Gonzalez and W. Mack Grady, “Battery energy storage for enabling integration of distributed solar power generation”, IEEE Trans. Smart Grid, vol. 3, no. 2, pp. 850-857, 2012.
20
[21] D.Q. Hung, N. Mithulananthan, R. Bansal, “Integration of PV and BES units in commercial distribution systems considering energy loss and voltage stability”, Appl. Energy, vol. 113, pp. 1162-1170,2014.
21
[22] J. Tant, F. Geth, D. Six, P. Tant, and J. Driesen, "Multi-objective battery storage to improve PV integration in residential distribution grids", IEEE Trans. Sustainable Energy, vol. 4, no. 1, pp. 182-191, 2013.
22
[23] R. Khalilpour, A. Vassallo, “Planning and operation scheduling of PV-battery systems: A novel methodology”, Renewable Sustainable Energy Reviews, vol. 53, pp. 194-208, 2016.
23
[24] Y. Zheng, Z.Y. Dong, F.J. Luo, “Optimal allocation of energy storage system for risk mitigation of DISCOs with high renewable penetrations”, IEEE Trans. Power Syst., vol. 29, pp. 212-220, 2014.
24
[25] A. Gabash, “Flexible optimal operation of battery storage systems for energy supply networks”, IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2788-2797, 2013.
25
[26] R. Afshan, J. Salehi, “Optimal operation of distribution networks with presence of distributed generations and battery energy storage systems considering uncertainties and risk analysis”, J. Renewable Sustainable Energy, vol. 9, pp. 12, 2017.
26
[27] V. H. Johnson, “Battery Performance Models in ADVISOR,” J. Power Sources, vol. 110, pp. 321-329, 2002.
27
[28] M. Amelin, “On Monte Carlo simulation and analysis of electricity markets”, Ph.D. dissertation, Dept. Elect. Eng. Royal Inst. Tech, Stockholm, 2004.
28
[29] R. Billinton, H. Chen, R. Ghajar, “Time-series models for reliability ealuation of power systems including wind energy”, Microelectron. Reliab., vol. 36, pp.1253-1291, 1996.
29
[30] M. Dicorato, G. Forte, M. Trovato, and E. Caruso, “Risk-constrained profit maximization in day-ahead electricity market”, IEEE Trans. Power Syst., vol. 24, no. 3, pp. 1107-1114, 2009.
30
ORIGINAL_ARTICLE
An Intelligent Method Based on WNN for Estimating Voltage Harmonic Waveforms of Non-monitored Sensitive Loads in Distribution Network
An intelligent method based on wavelet neural network (WNN) is presented in this study to estimate voltage harmonic distortion waveforms at a non-monitored sensitive load. Voltage harmonics are considered as the main type of waveform distortion in the power quality approach. To detect and analyze voltage harmonics, it is not economical to install power quality monitors (PQMs) at all buses. The cost associated with the monitoring procedure can be reduced by optimizing the number of PQMs to be installed. The main aim of this paper is to further reduce the number of PQMs through recently proposed optimum allocation approaches. An estimator based on WNN is presented in this study to estimate voltage-harmonic waveforms at a non-monitored sensitive load using current and voltage at a monitored location. Since capacitors and distributed generations (DGs) have a special role in distribution networks, they are considered in this paper and their effects on the harmonic voltage waveform estimator are evaluated. The proposed technique is examined on the IEEE 37-bus network. Results indicate the acceptable high accuracy of the WNN estimator.
http://joape.uma.ac.ir/article_633_c5d439eb9e9635a9c48b7f0a7d431a5b.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
13
22
10.22098/joape.2018.3533.1280
Distributed network
Power quality monitoring
Voltage harmonic
Wavelet neural network
A.
ِDeihimi
a_deihimi@yahoo.com
true
1
Bu-Ali Sina University, Department of Electrical Engineering
Bu-Ali Sina University, Department of Electrical Engineering
Bu-Ali Sina University, Department of Electrical Engineering
LEAD_AUTHOR
A.
Rahmani
rahmani878@gmail.com
true
2
Bu-Ali Sina University, Department of Electrical Engineering,
Bu-Ali Sina University, Department of Electrical Engineering,
Bu-Ali Sina University, Department of Electrical Engineering,
AUTHOR
[1] A. Kusko, M.T. Thompson, “Power Quality in Electrical Systems,” McGraw-Hill, 2007.
1
[2] H. Dehghani, B. Vahidi, R. Naghizadeh, S.H. Hosseinian, “Power quality disturbance classification using a statistical and wavelet-based hidden Markov model with Dempster-Shafer algorithm,” Electr. Power Energy Syst., vol. 47, pp. 368-377, 2013.
2
[3] A. Kazemi, A. Mohamed, H. Shareef, H. Zayandehroodi, “Optimal power quality monitor placement using genetic algorithm and Mallow’s Cp,” Electr. Power Energy Syst., vol. 53, pp. 564-575, 2013.
3
[4] C. F. M. Almeida and N. Kagan, “Harmonic state estimation through optimal monitoring systems,” IEEE Trans. Smart Grid., vol. 4, no. 1, pp. 467-478, 2013.
4
[5] A. Farzanehrafat, N. R. Watson, “Power quality state estimator for smart distribution grids,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2183-2191, 2013.
5
[6] S. G. Ghiocel, J. H. Chow, G. Stefopoulos, B. Fardanesh, D. Maragal, B. Blanchard, M. Razanousky, and D. B. Bertagnolli, “Phasor-measurement-based state estimation for synchrophasor data quality improvement and power transfer interface monitoring,” IEEE Trans. Power Syst., vol. 29, no. 2, pp. 881-888, 2014.
6
[7] E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, “GSA: a gravitational search algorithm,” Inform. Sci., vol. 179, no. 13, pp. 2232-2248, 2009.
7
[8] D.J. Won, S.I. Moon, “Optimal number and locations of power quality monitors considering system topology,” IEEE Trans. Power Delivery, vol. 23, pp. 288-295, 2008.
8
[9] Y.Y. Hong, Y.Y. Chen, “Placement of power quality monitors using enhanced genetic algorithm and wavelet transform,” IET Gener. Transm. Distrib., vol. 5, pp. 461-466, 2011.
9
[10] A. Deihimi, A. Momeni, “Neural estimation of voltage-sag waveforms of non-monitored sensitive loads at monitored locations in distribution networks considering DGs,” Electr. Power Syst. Res., vol. 92, pp. 123-137, 2012.
10
[11] J. Liu, F. Ponci, A. Monti, C. Muscas, P. A. Pegoraro, and S. Sulis, “Optimal meter placement for robust measurement systems in active distribution grids,” IEEE Trans. Instrum. Meas., vol. 63, no. 5, pp. 1096-1105, 2014.
11
[12] M. G. Damavandi, V. Krishnamurthy, and J. R. Martí, “Robust meter placement for state estimation in active distribution systems,” IEEE Trans. Smart Grid, vol. 6, no. 4, pp.1972-1982, 2015.
12
[13] R. Kazemzadeh, E. Najafi Aghdam, M. Fallah, Y. Hashemi, “Performance scrutiny of two control schemes based on DSM and HB in active power filter,” J. Oper. Autom. Power Eng., vol. 2, no. 2, pp. 103-112, 2014.
13
[14] A. Deihimi, A. Rahmani, “Application of echo state network for harmonic detection in distribution networks,” IET Genera. Transm. Distrib., vol. 11, no. 5, pp. 1094-1101, 2017.
14
[15] S.K. Jain, S. N. Singh, “Low-order dominant harmonic estimation using adaptive wavelet neural network,” IEEE Trans. Ind. Electron., vol. 61, pp. 428-435, 2014.
15
[16] B. Renders, K. D. Gussemé, W.R. Ryckaert, K. Stockman, L. Vandevelde, M.H.J. Bollen, “Distributed generation for mitigating voltage dips in low-voltage distribution grids,” IEEE Trans. Power Delivery, vol. 23, pp. 1581-1588, 2008.
16
[17] R. Song, “Multiple attribute decision making method andapplication based on wavelet neural network,” Control Decis., vol. 15, no. 6, pp. 765-768, 2000.
17
[18] Q. Zhang, A. Benveniste, “Wavelet networks,” IEEE Trans. Neural Netw., vol. 3, no. 6, pp. 889-898, 1992.
18
[19] W.H. Kersting, “Radial distribution test feeders,” IEEE/PES., Winter Meeting, 2001.
19
[20] A. Bertani, C. Bossi, F. Fornari, S. Massucco, S. Spelta, F. Tivegna, “A micro turbine generation system for grid connected and islanding operation,” IEEE PSCE., New York, 2004.
20
[21] R.C. Dugan, M.F. McGranaghan, S. Santo, H.W. Beaty, “Electrical Power System Quality,” 2nd Ed., McGraw-Hill, New York, 2003.
21
[22] J.S. LAI, T.S. KEY, “Effectiveness of harmonic mitigation equipment for commercial office buildings,” IEEE Trans. Ind. Appl., vol. 33, pp. 1065-1110, 1997.
22
[23] N.R. Watson, “Power quality state estimation,” Eur. Trans. Electr. Power, vol. 20, pp. 19-33, 2010.
23
[24] B. Mohammadi, A. Mokari, H. Seyedi, S. Ghasemzadeh, “An improved under-frequency load shedding scheme in distribution networks with distributed generation,” J. Oper. Autom. Power Engin., vol. 2, no. 1, pp. 22-31, 2007.
24
[25] Y.G. Hegazy, M.A. Salama, “Identification the relationship between voltage harmonic distortion and the load of harmonic producing devices in distribution networks,” IEEE Can. Conf. Electr. Comput. Engin., pp. 669-674, 1994.
25
[26] H. E. Mazin, W. Xu, “Determining the harmonic impacts of multiple harmonic-producing loads,” IEEE Trans. Power Delivery, vol. 26, 1187-1195, 2011.
26
ORIGINAL_ARTICLE
Increasing the Efficiency of the Power Electronic Converter for a Proposed Dual Stator Winding Squirrel-Cage Induction Motor Drive Using a Five-Leg Inverter at Low Speeds
A dual stator winding squirrel-cage induction motor (DSWIM) is a brushless single-frame induction motor that contains a stator with two isolated three-phase windings wound with dissimilar numbers of poles. Each stator winding is fed by an independent three-phase inverter. The appropriate efficiency of this motor is obtained when the ratio of two frequencies feeding the machine is equal to the ratio of the number of poles. In the vector control method at low speeds, flux is difficult to estimate because the voltage drop on the stator resistance is comparable with the input stator voltage, disturbing the performance of the motor drive. To solve the abovementioned problem, researchers have benefited from the free capacity of the two windings of the stator. This makes the motor deviate from its standard operating mode at low speeds. The main purpose of this paper is reducing the power losses of the inverter unit in the DSWIM drive at low speeds via the proposed control method and a five-leg inverter. This paper deals with two topics: 1. Using the idea of rotor flux compensation at low speeds, the motor works in its standard operating mode. Therefore, the power losses of the utilized power electronic converters are also reduced to a considerable extent; and 2. Reduction in capital cost can be achieved by utilizing a five-leg power electronic converter. The proposed methods are simulated in MATLAB/Simulink software, and the results of simulation confirm the assumptions.
http://joape.uma.ac.ir/article_634_bea20854ea2da190bf8e2ff84b7db704.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
23
39
10.22098/joape.2018.2990.1249
Dual stator winding
five-leg inverter
induction machine
low speed
vector control
H.
Moayedirad
hojatrad@birjand.ac.ir
true
1
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
AUTHOR
M. A.
Shamsi Nejad
mshamsi@birjand.ac.ir
true
2
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran,
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran,
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran,
LEAD_AUTHOR
[1] G. K. Singh, “Multi-phase induction machine drive research-a survey,” Electr. Power Syst. Res., vol. 61, no. 2, pp. 139-147, 2002.
1
[2] M. Bigdeli1, D. Azizian, and E. Rahimpour, “An improved big bang-big crunch algorithm for estimating,” J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 83-92, 2016.
2
[3] E. Abdi, M. R. Tatlow, R. A. McMahon, and P. J. Tavner, “Design and performance analysis of a 6 MW medium-speed brushless DFIG,” in Proce. of the Renewable Power Gener. Conf., 2013, pp. 1-4.
3
[4] P. C. Roberts, “A study of brushless doubly-fed (induction) machines,” PhD dissertation, Dept. Eng., Univ. Cambridge, 2005.
4
[5] A. R. Muñoz and T. A. Lipo, “Dual stator winding induction machine drive,” IEEE Trans. Ind. Appl., vol. 36, no. 5, pp. 1369-1379, 2000.
5
[6] J. M. Guerrero and O. Ojo, “Total airgap flux minimization in dual stator winding induction machines,” IEEE Trans. Power Electr., vol. 24, no. 3, pp. 787-795, Mar. 2009.
6
[7] R. Ueda, T. Sonoda, M. Ichikawa, and K. Koga, “Stability analysis in induction motor driven by V/f controlled general purpose inverter,” IEEE Trans. Ind. Appli., vol. 82, no. 2, pp. 472-481, 1992.
7
[8] R. Bojoi, F. Farina, G. Griva, and F. Profumo, “Direct torque control for dual three-phasei motor drives,” IEEE Trans. Ind. Appl., vol. 41, no. 6, pp. 1627-1636, 2005.
8
[9] K. Pienkowski, “Analysis and control of dual stator winding induction motor,” Archi. Electr. Eng., vol. 61, no. 3, pp. 421-438, 2012.
9
[10] S. Basak, and C. Chakraborty, “Dual stator winding induction machine: problems, progress and future scope,” IEEE Trans. Ind. Electron., vol. 62, no. 7, pp. 4641-4652, 2015.
10
[11] Z. Wu, O. Ojo, and J.Sastry, “High-performance control of a dual stator winding DC power induction generator,” IEEE Trans. Ind. Appl., vol. 43, no. 2, pp. 582-592, 2007.
11
[12] O. Ojo and Z. Wu, “Speed control of a dual stator winding induction machine,” in Proc. Of the IEEE Applied Power Electronics Conference, 2007, pp. 229-235.
12
[13] M. B. Slimene, M. L. Khlifi, M. B. Fredj, and H. Rehaoulia, “Indirect field-oriented control for dual stator induction motor drive,” Proc. Of the 10nd Int. Multi-Conf. Syst., Signals Devices, pp. 18-21, 2013.
13
[14] H. Moayedirad, M. A. Shamsinejad, and M. Farshad, “Neural control of the induction motor drive: robust against rotor and stator resistances variations and suitable for very Low and high speeds,” Iran J. Elect. Com. Eng., vol. 9, no. 2, pp 107-113, 2011.
14
[15] H. Moayedirad , M. Farshad, and M.A. Shamsinejad, “Improvement of speed profile in induction motor drive using a new idea of PWM pulses generation base on artificial neural networks,” Inte. Syst. Electr. Eng., vol. 2, no. 4, pp 35-46, 2012.
15
[16] H. Moayedirad, M. A. Shamsinejad, and M. Farshad, “Improvement of induction motor drive operation in low and high speeds using rotor flux compensation,” J. Iran Association. Electr. & Electron. Eng., vol. 9, no. 2, pp. 59-64, 2012.
16
[17] D. G. Holmes, B. P. McGrath, and S. G. Parker, “Current regulation strategies for vector-controlled induction motor drives,” IEEE Trans. Ind. Electro., vol. 59, no. 10, pp. 3680-3689, 2012.
17
[18] B. M. Joshi and M. C. Chandorkar, “Vector control of two-motor single-inverter induction machine drives,” Electr. Power Compo. Syst., vol. 42, no. 11, pp. 1158-1171, 2014.
18
[19] R. D. Lorenz and D. B. Lawson, “A simplified approach to continuous on-line tuning of field oriented induction motor drives,” IEEE Trans. Ind. Appl., vol. 26, no. 3, pp. 420-424, 1990.
19
[20] M. P. Kazmierkowski, “A novel vector control scheme for transistor PWM inverter-fed induction motor drive,” IEEE Trans. Ind. Appli., vol. 38, no. 1, pp. 41-47, 1991.
20
[21] H. M. Kojabadi, L. Chang, and R. Doraiswami, “A MRAS-based adaptive pseudoreduced-order flux observer for sensorless induction motor drives,” IEEE Trans. Power Electron., vol. 20, no. 4, pp. 930-176, 2005.
21
[22] M. S. Zaky and M. K. Metwaly, “Sensorless torque/speed control of induction motor drives at zero and low frequencies with stator and rotor resistance estimations,” IEEE J. Emerg. and Sel. Top. Power Electr., vol. 4, no. 4, pp. 1416-1429, 2016.
22
[23] C. P. Salomon et al., “Induction motor efficiency evaluation using a new concept of stator resistance,” IEEE Trans. Instrum. Meas., vol. 64, no. 11, pp. 2908-2917, 2015.
23
[24] J. Chen and J. Huang, “Online decoupled stator and rotor resistances adaptation for speed sensorless induction motor drives by a time-division approach,” IEEE Trans. Power Electr., vol. 32, no. 6, pp. 4587-4599, 2017.
24
[25] A. Dey, B. Singh, B. Dwivedi, and D. Chandra, “Vector control of three-phase induction motor using artificial intelligent technique,” ARPN J. Eng. and Appl. Sci., vol. 4, no. 4, pp. 57-67, 2009.
25
[26] Z. G. Yin, C. Zhao , and Y. R. Zhong J. Liu, “Research on robust performance of speed-sensorless vector control for the induction motor Using an interfacing multiple-model extended kalman filter,” IEEE Trans. Power Electr., vol. 29, no. 6, pp. 3011 - 3019, 2014.
26
[27] I. M. Alsofyani and N. R. N. Idris, “Simple flux regulation for improving state estimation at very low and zero speed of a speed sensorless direct torque control of an induction motor,” IEEE Trans. Power Electr., vol. 31, no. 4, pp. 3027- 3035, 2016.
27
[28] I. M. Alsofyani and N. R. N. Idris, “Lookup-table-based DTC of induction machines with improved flux regulation and extended kalman filter state estimator at low-speed operationr,” IEEE Trans. Ind. Inform., vol. 12, no. 4, pp. 1412-1425, 2016.
28
[29] D. Stojic, M. Milinkovic, S. Veinovic, and I. Klasnic, “Improved stator flux estimator for speed sensorless induction motor drives,” IEEE Trans. Power Electr., vol. 30, no. 4, pp. 2363 - 2371, 2015.
29
[30] M. Jones, S. N. Vukosavic, D. Dujic, E. Levi, and P. Wright, “Five-leg inverter PWM technique for reduced switch count two-motor constant power applications,” IET Electr. Power Appl., vol. 2, no. 5, pp. 275-287, 2008.
30
[31] S. Laali, E. Babaei, and M. B. B. Sharifian, “Reduction the number of power electronic devices of a cascaded multilevel inverter based on new general topology,” J. Oper. Autom. Power Eng., vol. 2, no. 2, pp. 81-90, 2014.
31
[32] K. Oka, Y. Nozawa, and K. Matsuse, “An improved method of voltage utility factor for PWM control of a five-leg inverter in two induction motor drives,” IEEJ Trans. Electr. and Electron. Eng., vol. 1, no. 1, pp. 108-111, 2006.
32
[33] C. S. Lim, N. A. Rahim, W. P. Hew, and E. Levi, “Model predictive control of a two-motor drive with five-leg-inverter supply,” IEEE Trans. Ind. Electr., vol. 60, no. 1, pp. 54-65, 2013.
33
[34] Y. Mei and S. Feng, “An optimized modulation method for a five-leg-inverter for dual induction motor drives,” in Proce. of the IPEMC-ECOC, 2016, pp. 660-663.
34
[35] S. Dangeam and V. Kinnares, “Five-leg voltage source inverter for driving two single-phase induction motors,” in Proce. of the 17th Int. Conf. Electr. Mach. Syst., 2014, pp. 156 - 161.
35
[36] O. Ojo and Z.Wu, “Modeling of a dual-stator-winding induction machine including the effect of main flux linkage magnetic saturation,” IEEE Trans. Ind. Appl., vol. 44, no. 4, pp. 1099-1107, 2008.
36
[37] B. K. Bose, Modern Power Electronics and AC Drives. Upper Saddle River, NJ: Prentice-Hall, 2002.
37
[38] J. Hu and B. Wu, “New integration algorithms for estimating motor flux over a wide speed range,” IEEE Trans. Power Electr., vol. 13, no. 5, pp.969-976, 1998.
38
[39] E. Babaei, M. H. Babayi, E. ShokatiAsl, and S. Laali, “A new topology for Z-source inverter based on switched-inductor and boost Z-source inverter,” J. Oper. Autom. Power Eng., vol. 3, no. 2, pp. 167-184, 2015.
39
[40] D. W. Novotny and T. A. Lipo, Vector Control and Dynamics of AC Drives. Oxford University Press, 1997.
40
[41] Z. Zhou, M. S. Khanniche, P. Igic, S. M. Towers, and P. A. Mawby, “Power loss calculation and thermal modelling for a three phase inverter drive system,” J. Electri. Syst., vol. 1, no. 4, pp.33-46, 2005.
41
[42] J. Pou, D. Osorno, J. Zaragoza, S. Ceballos, and C. Jaen, “Power losses calculation methodology to evaluate inverter efficiency in electrical vehicles,” in Proce. of the 17th Int. Conf. Work. Compab. Power. Electron., 2011, pp. 400-409.
42
ORIGINAL_ARTICLE
Characterization of Power Transformer Electromagnetic Forces Affected by Winding Faults
Electromagnetic forces in power transformer windings are produced by interaction between the leakage fluxes and current passing them. Since the leakage flux distribution along the windings height is in two axial and radial directions, so the electromagnetic forces have two components, radial and axial. There is a risk that a large electromagnetic force due to the short circuit or inrush currents, can cause the windings to be deform, rupture, and/or displace, if the transformer and winding holders structure is not designed or assembled properly. Also, these mechanical changes can damage insulation between two or more adjacent turns of a winding and so, produce the local inter-turn fault. Occurrence of any fault in windings will change the electromagnetic force distribution in transformers and will cause developing secondary faults. Hence, this contribution is aimed at characterizing the electromagnetic forces behavior in power transformers and determines the changes of force values after occurring winding mechanical and inter-turn. The study keeps at disposal a two-winding, three phase, 8 MVA power transformer, on their windings faults are imposed and investigated through the FEM analysis. The accuracy of the created FEM model is firstly validated using analytical methods for transformer healthy condition, and then the winding shorted turn fault along with the mechanical faults are considered using 3D FEM model. The extracted characteristic signatures attained to different type of winding faults is expected to be useful at the design stage of power transformers.
http://joape.uma.ac.ir/article_635_cfc6367c365eaeb00851ee642eb372ff.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
40
49
10.22098/joape.2018.2436.1210
Power transformer
Electromagnetic forces
Finite element method (FEM)
Interturn fault
Winding mechanical fault
V.
Behjat
behjat@azaruniv.edu
true
1
Department of Electrical Engineering, Azarbaijan Shahid Madani university, Tabriz, Iran.
Email: behjat@azaruniv.edu
Department of Electrical Engineering, Azarbaijan Shahid Madani university, Tabriz, Iran.
Email: behjat@azaruniv.edu
Department of Electrical Engineering, Azarbaijan Shahid Madani university, Tabriz, Iran.
Email: behjat@azaruniv.edu
LEAD_AUTHOR
A.
Shams
alirezashams.un@gmail.com
true
2
Department of Electrical Engineering, Engineering Faculty, Azarbaijan Shahid Madani University, Tabriz, Iran
Department of Electrical Engineering, Engineering Faculty, Azarbaijan Shahid Madani University, Tabriz, Iran
Department of Electrical Engineering, Engineering Faculty, Azarbaijan Shahid Madani University, Tabriz, Iran
AUTHOR
V.
Tamjidi
v.tamjidi@azaruniv.edu
true
3
Department of Electrical Engineering, Engineering Faculty, Shahid Madani University, Tabriz, Iran
Department of Electrical Engineering, Engineering Faculty, Shahid Madani University, Tabriz, Iran
Department of Electrical Engineering, Engineering Faculty, Shahid Madani University, Tabriz, Iran
AUTHOR
[1] Zhang, Z.W., Tang, W.H., Ji, T.Y., Wu, Q.H., “Finite-element modeling for analysis of radial deformations within transformer windings,” IEEE Trans. Power Deliv., vol. 25, no. 5, pp. 2297-2305, 2014
1
[2] A.A. Adly, “Computation of inrush current forces on transformer windings,” IEEE Trans. Magn., vol. 37, no. 4, pp. 2855-2857, 2001.
2
[3] G. B. Kumbhar and S.V. Kulkarni, “Analysis of short-circuit performance of split-winding transformer using coupled field-circuit approach,” IEEE Trans. Power Deliv., vol. 22, no. 2, pp. 936-943, 2007.
3
[4] J. Faiz, B.M. Ebrahimi, and T. Noori, “Three-and two-dimensional finite-element computation of inrush current and short-circuit electromagnetic forces on windings of a three-phase core-type power transformer,” IEEE Trans. Magn., vol. 44, no. 5, pp. 590-597, 2008.
4
[5] H.M. Ahn, J.Y. lee, J.K. Kim, and Y.H. Oh, “Finite-element analysis of short-circuit electromagnetic force in power transformer,” IEEE Trans. Ind. Appl., vol. 47, no. 3, pp. 1267-1272, 2011.
5
[6] A.G. Kladas, M.P. Papadopoulos, and J.A. Tegopoulos, “Leakage flux and force calculation on power transformer windings under short-circuit: 2D and 3D models based on the theory of images and the finite element method compared to measurements,” IEEE Trans. Magn., vol. 30, no. 5, pp. 3487-3490, 1994.
6
[7] A.C. De Azevedo, I. Rezende, A.C. Delaiba, J.C. De Oliveira, “Investigation of transformer electromagnetic forces caused by external faults using FEM,” In Proc. of the IEEE PES Transm. Distrib. Conf. Exposition: Lat. Am., 2006.
7
[8] Y.Q. Tang, J.Q. Qiao, and ZH. Xu, “Numerical calculation of short circuit electromagnetic forces on the transformer winding,” IEEE Trans. Magn., vol. 26, no. 2, pp. 1039-1041, 1990.
8
[9] T. Renyuan, L. Yan, L. dake, T. Lijian, “Numerical calculation of 3D eddy current field and short circuit electromagnetic force in large transformers,” IEEE Trans. Magn., vol. 28, no. 2, pp. 1418-1421, 1992.
9
[10] S. Jamali, M. Ardebili, and K. Abbaszadeh, “Calculation of short circuit reactance and electromagnetic forces in three phase transformer by finite element method,” Electr. Mach. Syst. In Proc. Of the 8th Int. Conf., vol. 3, 2005.
10
[11] M. Ardebili, K. Abbaszadeh, S. Jamali, H.A. Toliyat, “Winding arrangement effects on electromagnetic forces and short-circuit reactance calculation in power transformers via numerical and analytical methods,” In Proc. of the IEEE 12th Biennial Conf., 2006.
11
[12] M., Zhiqiang and Z. Wang, “The analysis of mechanical strength of HV winding using finite element method. Part I Calculation of electromagnetic forces,” Univ. Power Eng. Conf., vol. 1, 2004.
12
[13] N.Y. Abed and O.A. Mohammed, “Modeling and characterization of transformers internal faults using finite element and discrete wavelet transforms,” IEEE Trans. Magn., vol. 43, no. 4, pp. 1425-1428, 2007.
13
[14] K. Guven and T. Gundogdu, “Effect of the tap winding configurations on the electromagnetic forces acting on the concentric transformer coils,” In Proc. of the 3rd Int. Conf. Electr. Power Energy Convers. Syst., 2013.
14
[15] J. F. Araujo, E. G. Costa, F. L. M. Andrade, A. D. Germano, and T. V. Ferreira, “Methodology to Evaluate the Electromechanical Effects of Electromagnetic Forces on Conductive Materials in Transformer Windings Using the Von Mises and Fatigue Criteria,” IEEE Trans. Power Deliv., vol. 31, no. 5, pp. 2206-2214, 2016.
15
[16] D. Geißler; T. Leibfried, “Short-Circuit Strength of Power Transformer Windings-Verification of Tests by a Finite Element Analysis-Based Model,” IEEE Trans. Power Deliv., vol. 32, no. 4, pp. 1705-1712, 2017.
16
[17] N. Hashemnia, A. Abu-Siada and S. Islam, “Improved power transformer winding fault detection using FRA diagnostics – part 1: axial displacement simulation,” IEEE Trans. Dielect. Elect. Insul., vol. 22, no. 1, pp. 556-563, 2015.
17
[18] S.V. Kulkarni and S.A. Khaparde, Transformer engineering: design and practice, vol. 25. CRC Press, 2nd ed, 2012.
18
[19] W. J McNutt, W.M.Johnson, and R.A.Nelson, “Power transformer short-circuit strength-requirements, designer, and demonstration,” IEEE Trans. Power Appl. Syst., PAS-89, no. 8, pp. 1955-1969, 1970.
19
[20] R.M. Del-Vecchio, B. Poulin, and P.T. Feghali, Transformer design principles: with applications to core-form power transformers, FL: CRC Press, US, 2nd ed, 2010.
20
[21] J.A.S.B. Jayasinghe and Z.D. Wang, “Winding movement in power transformers: a comparison of FRA measurement connection methods,” IEEE Trans. Dielect. Elect. Insul., vol. 13, no. 6, pp.1342-1349, 2006.
21
[22] M. Bagheri, S.N. Mohammad, and T. Blackburn, “Advanced transformer winding deformation diagnosis: moving from off-line to on-line,” IEEE Trans. Dielect. Elect. Insul., vol. 19, no. 6, pp. 1860-1870, 2012.
22
[23] IEEE Guide for Protective Relay Applications to Power Transformers, IEEE C37.91-2000.
23
[24] L.M.R. Oliveira and A.J. Cardoso, “On-line diagnostics of transformer winding insulation failures, by Park's Vector Approach,” In Proc. of the 9th Int. Electr. Insul. Conf., 2001.
24
[25] K.L. Butler-Purry, M. Bagriyanik, M.J. Mousavi, P. Palmer-Buckle, “Experimental investigation of internal short circuit faults leading to advanced incipient behavior and failure of a distribution transformer,” In Proce. IEEE PES Power Syst. Conf. Expos., 2004.
25
[26] V. Behjat and A. Vahedi, “An experimental approach for investigating low-level interturn winding faults in power transformers,” Spring Electr. Eng., vol. 95, no. 2, pp. 135-145, 2013.
26
[27] B. Ravindranath and M. Chander, Power syst. Protection. switchgear, New Age International, 1977.
27
[28] Y.G. Paithankar, S.R. Bhide, Fundamentals of Power System Protection, PHI Learning Pvt. Ltd. 2004.
28
[29] A. Vahedi and V. Behjat, “Online monitoring of power transformers for detection of internal winding short circuit faults using negative sequence analysis,” Eur. Trans. Electr. Power, vol. 21, no. 1, pp. 196-211, 2011.
29
[30] M.R. Feyzi and M. Sabahi, “Finite element analysis of short circuit forces in power transformers with asymmetric conditions,” In Proce. of the IEEE Int. Symp. Ind. Electron., pp. 576-581, 2008.
30
[31] A.C. Franklin, D.P. Franklin, The J&P TransformerBook, Butterworth Ltd, London, 1993.
31
The Short Circuit Performance of Power Transformers, Brochure CIGRE WG 12.19, 2002.
32
ORIGINAL_ARTICLE
Coordinated resource scheduling in a large scale virtual power plant considering demand response and energy storages
Virtual power plant (VPP) is an effective approach to aggregate distributed generation resources under a central control. This paper introduces a mixed-integer linear programming model for optimal scheduling of the internal resources of a large scale VPP in order to maximize its profit. The proposed model studies the effect of a demand response (DR) program on the scheduling of the VPP. The profit of the VPP is calculated considering different components including the income from the sale of electricity to the network and the incentives received by the renewable resources, fuel cost, the expense of the purchase of electricity from the network and the load curtailment cost during the scheduling horizon. The proposed model is implemented in a large scale VPP that consists of five plants in two cases: with and without the presence of the DR. Simulation results show that the implementation of the DR program reduces the operation cost in the VPP, therefore increasing its profit.
http://joape.uma.ac.ir/article_636_271e7836b49dcc5d545da388f996ddd0.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
50
60
10.22098/joape.2018.3153.1257
Virtual power plant
Demand response
Distributed energy resources
storage, Mixed-integer linear programming
H.
M. Samakoosh
hajarmohammadinia@gmail.com
true
1
Mazandaran University of Science and Technology
Mazandaran University of Science and Technology
Mazandaran University of Science and Technology
AUTHOR
M.
Jafari-Nokandi
m.jafari@nit.ac.ir
true
2
Noshirvani University of Technology
Noshirvani University of Technology
Noshirvani University of Technology
LEAD_AUTHOR
A.
Sheikholeslami
asheikh@nit.ac.ir
true
3
Noshirvani University of Technology
Noshirvani University of Technology
Noshirvani University of Technology
AUTHOR
[1] L. I. Dulău, M. Abrudean, and D. Bică, “Effects of Distributed Generation on Electric Power Systems,” Procedia Tech., vol. 12, pp. 681-686, 2014.
1
[2] M. A .Tajeddini, A. Rahimi-Kian, and A. Soroudi, “Risk averse optimal operation of a virtual power plant using two stage stochastic programming,” Energy, vol. 73, pp. 958-967, 2014.
2
[3] S. R. Dabbagh and M. K. Sheikh-El-Eslami, “Risk-based profit allocation to DERs integrated with a virtual power plant using cooperative Game theory,” Electr. Power Syst. Res., vol. 121, pp. 368–378, 2015.
3
[4] S. Pazouki, M. R. Haghifam, and S. Pazouki “Transition from fossil fuels power plants to ward virtual power plants of distribution networks,”in Proc. of the EPDC, Karaj, Iran, 2016,pp. 82-86.
4
[5] P. Asmus, “Micro grids, virtual power plants and our distributed energy future,” Electr. J., vol. 23, no. 8, pp. 72-82, 2010.
5
[6] M. Peik-Herfeh, H. Seifi, and M. K. Sheikh-El-Eslami, “Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method,” Electr. Power Energy Syst., vol. 44, no. 1, pp. 88-98, 2013.
6
[7] H. Pandzic, J. M. Morales, A. J Conejo, and I. Kuzle, “Offering model for a virtual power plant based on stochastic programming,” App. Energy, vol.105, pp. 282-292, 2013.
7
[8] E. Mashhour and S. M. Moghaddas-Tafreshi, “Bidding strategy of virtual power plant for participating in energy and spinning reserve markets - part I: problem formulation,” IEEE Trans. Power Syst., vol. 26, no. 2, pp. 949-56, 2011.
8
[9] E. Mashhour and S. M. Moghaddas-Tafreshi, “Bidding strategy of virtual power plant for participating in energy and spinning reserve markets: part II: numerical analysis,” IEEE Trans. Power Syst., vol. 26, no. 2, pp. 957-64, 2011.
9
[10] P. Faria, J. Soares, Z. Vale, H. Morais, and T. Sousa, “Modified particle swarm optimization applied to integrated demand response and DG resources scheduling,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 606-616, 2013.
10
[11] M. Giuntoli and D. Poli, “Optimal thermal and electrical scheduling of a large scale virtual power plant in the presence of energy storage,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 942-955, 2013.
11
[12] A. GH. Zamani, A. Zakariazadeh, S. Jadid, and A. kazemi, “Stochastic operational scheduling of distributed energy resources in a large scale virtual power plant,” Int. J. Electr. Power Energy Syst., vol. 82, pp.608-620, 2016.
12
[13] A. Yousefi, T. T. Nguyen, H. Zareipour, and O. P. Malik, “Congestion management using demand response and FACTS devices,” Electr. Power Energy Syst., vol. 37, no. 1, pp. 78-85, 2012.
13
[14] Federal Energy Regulatory Commission Staff, “Assessment of demand response and advanced metering,” FERC, 2007.
14
[15] N. Çiçek and H. Deliç, “Demand response management for smart grids with wind power,” IEEE Trans. Sust. Energy, vol. 6, no. 2, pp. 625-634, 2015.
15
[16] Q. Wang, J. Wang, and Y. Guan, “Stochastic unit commitment with uncertain demand response,” IEEE Trans. Power Syst.,vol. 28, no. 1, pp. 562-563, 2013.
16
[17] M. Fathi and H. Bevrani, “Adaptive energy consumption scheduling for connected micro grids under demand uncertainty,” IEEE Trans. Power Del., vol. 28, no. 3, pp. 1576-1583, 2013.
17
[18] P. R. Thimmapuram and J. Kim, “Consumers’ price elasticity of demand modeling with economic effects on electricity markets using an agent based model,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 390-397, 2013.
18
[19] E. Dehnavi, H. Abdi, and F. Mohammadi, “Optimal emergency demand response program integrated with multi-objective dynamic economic emission dispatch problem”,J. Oper. Autom. Power Eng., vol. 4, no. 1, pp.29-41, 2016.
19
[20] H. Arasteh, M. S. Sepasian and V. Vahidinasab, “Toward a smart distribution system expansion planning by considering demand response resources,”J. Oper. Autom. Power Eng., vol. 3, no. 2, pp.116-130,2015.
20
[21] L. Bajracharyay, S. Awasthi, S. Chalise, T. M. Hansen, and R. Tonkoski, “Economic analysis of a data center virtual power plant participating in demand response,” Proc. Powe Energy Soc. General Meeting, Boston, MA, USA, 2016, pp. 1-5.
21
[22] H.T. Nguyen and L.B. Le, “Bidding strategy for virtual power plant with intraday demand response rxchange market using stochastic programming,” Proc. IEEE Int. Conf. Sustain. Energy Tech., Hanoi, Vietnam, 2016, pp. 96-101.
22
[23] A. Mnatsakanyan and S. W. Kennedy, “A novel demand response model with an application for a virtual power plant,” IEEE Trans. Smart Grid, vol. 6, no. 1, pp. 230-237, 2014.
23
[24] Available at:
24
http://www.nyiso.com/public/markets_operations/market_data/graphs/index.jsp.
25
ORIGINAL_ARTICLE
A Novel Algorithm for Rotor Speed Estimation of DFIGs Using Machine Active Power based MRAS Observer
This paper presents a new algorithm based on Model Reference Adaptive System (MRAS) and its stability analysis for sensorless control of Doubly-Fed Induction Generators (DFIGs). The reference and adjustable models of the suggested observer are based on the active power of the machine. A hysteresis block is used in the structure of the adaptation mechanism, and the stability analysis is performed based on sliding mode conditions. Simulation and practical results show appropriate operation and speed tracking of the observer with regard to obtained stability conditions.
http://joape.uma.ac.ir/article_637_7fd6bbaeeeed5be43d72f475a244da9d.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
61
68
10.22098/joape.2018.2132.1200
Active power
Doubly Fed Induction Generator (DFIG)
MRAS-based observer
Stability analysis
R.
Ajabi-Farshbaf
r_ajabi@sut.ac.ir
true
1
Sahand University of Technology (SUT)
Sahand University of Technology (SUT)
Sahand University of Technology (SUT)
LEAD_AUTHOR
M. R.
Azizian
azizian@sut.ac.ir
true
2
Faculty of Electrical Engineering, Sahand University of Technology (SUT)
Faculty of Electrical Engineering, Sahand University of Technology (SUT)
Faculty of Electrical Engineering, Sahand University of Technology (SUT)
AUTHOR
V.
Yousefizad
v_yousefizad@sut.ac.ir
true
3
Sahand University of Technology (SUT)
Sahand University of Technology (SUT)
Sahand University of Technology (SUT)
AUTHOR
[1] A. Nafar, G. R. Arab Markadeh, A. Elahi, R. Pouraghababa. “Low voltage ride through enhancement based on improved direct power control of dfig under unbalanced and harmonically distorted grid voltage,” J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 16-28, 2016.
1
[2] W. Zhong, L. Guo-jie, S. Yuanzhang, and B. T. Ooi, “Effect of erroneous position measurements in vector-controlled doubly fed induction generator,” IEEE Trans. Energy Convers., vol. 25, no. 11, pp. 59-69, 2010.
2
[3] P. Vas, Sensorless Vector and Direct Torque Control. Oxford University Press, 1998.
3
[4] R. Cardenas, R. Pena, J. Proboste, G. Asher, J. Clare, “MRAS observer for sensorless control of standalone doubly fed induction generators,” IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 710-718, 2005.
4
[5] R. Cardenas, R. Pena, J. Clare, G. Asher, J. Proboste, “MRAS observers for sensorless control of doubly-fed induction generators,” IEEE Trans. Power Electr., vol. 23, no. 3, pp. 1075-1084, 2008.
5
[6] F. C. Dezza, G. Foglia, M. F. Iacchetti, R. Perini, “An MRAS observer for sensorless dfim drives with direct estimation of the torque and flux rotor current components,” IEEE Trans. Power Electr., vol. 27, no. 5, pp. 2576-2584, 2012.
6
[7] M. Iacchetti, M. S. Carmeli, F. Castelli Dezza, R. Perini, “A speed sensorless control based on a MRAS applied to a double fed induction machine drive,” Electr. Eng., vol. 91, no. 6, pp. 337-345, 2010.
7
[8] J. A. Cortajarena, J. De Marcos, “Neural network model reference adaptive system speed estimation for sensorless control of a doubly fed induction generator,” Electr. Power Comp. Syst., vol. 41, no. 12, pp. 1146-1158, 2013.
8
[9] M. P. Pattnaik and D. K. Kastha, “Adaptive speed observer for a stand-alone doubly fed induction generator feeding nonlinear and unbalanced loads,” IEEE Trans. Energy Convers., vol. 27, no. 4, pp. 1018-1026, 2012.
9
[10] P. K. Gayen, D. Chatterjee, S. K. Goswami, “Stator side active and reactive power control with improved rotor position and speed estimator of a grid connected DFIG (doubly-fed induction generator),” Energy, vol. 89, pp. 461-472, 2015.
10
[11] D. G. Forchetti, G. O. Garcia, M. I. Valla, “Adaptive observer for sensorless control of stand-alone doubly fed induction generator,” IEEE Trans. Ind. Elect., vol. 56, no. 10, pp. 4174-4180, 2009.
11
[12] H. Yongsu, K. Sungmin, and H. Jung-Ik, “Sensorless vector control of doubly fed induction machine using a reduced order observer estimating,” In Proc. of the IEEE Energy Convers. Cong. Expo., pp. 2623-2630, 2012.
12
[13] H. Serhoud, D. Benattous, “Sensorless optimal power control of brushless doubly-fed machine in wind power generator based on extended Kalman filter,” Int. J. Syst. Ass. Eng. Manage., vol. 4, no. 1, pp. 57-66, 2013.
13
[14] Y. Sheng, V. Ajjarapu, “A speed-adaptive reduced-order observer for sensorless vector control of doubly fed induction generator-based variable-speed wind turbines,” IEEE Trans. Energy Convers., vol. 25, no.3, pp. 891-900, 2010.
14
[15] A. Karthikeyan, C. Nagamani, G. S. Ilango, “A versatile rotor position computation algorithm for the power control of a grid-connected doubly fed induction generator,” IEEE Trans. Energy Convers., vol. 27, no.3, pp. 697-706, 2012.
15
[16] G. D. Marques, D. M. Sousa, “Sensorless direct slip position estimator of a DFIM based on the air gap pq - sensitivity study,” IEEE Trans. Ind. Electr., vol. 60, no.6, pp. 2442-2450, 2013.
16
[17] F. Akel, T. Ghennam, E. M. Berkouk, M. Laour, “An improved sensorless decoupled power control scheme of grid connected variable speed wind turbine generator,” Energy Convers. Manage., vol. 78, pp. 584-594, 2014.
17
[18] A. Karthikeyan, C. Nagamani, A. B. R. Chaudhury, G. S. Ilango, “Implicit position and speed estimation algorithm without the flux computation for the rotor side control of doubly fed induction motor drive,” IET Electr. Power Appl., vol. 6, no. 4, pp. 243-252, 2012.
18
[19] G. Abad, J. López, M. A. Rodríguez, L. Marroyo, G. Iwanski, “Dynamic modeling of the doubly fed induction machine,” doubly fed induction machine: modeling and control for wind energy generation, pp. 209-239, 2011.
19
[20] S. Maiti, C. Chakraborty, “MRAS-based speed estimation techniques for vector controlled double inverter-fed slipring induction motor drive,” Proc. 34th Ann. Conf. IEEE Ind. Elect., pp. 1275-1280, 2008.
20
[21] G. D. Marques, D. M. Sousa, “New sensorless rotor position estimator of a DFIG based on torque Calculations-Stability study,” IEEE Trans. Energy Convers., vol. 27, no. 1, pp. 196-203, 2012
21
[22] G. D. Marques, D. Mesquita e Sousa, “A new sensorless MRAS based on active power calculations for rotor position estimation of a DFIG,” Adv. Power Electr., vol. 2011, pp. 1-8, 2011.
22
[23] A. Mokhberdoran, A. Ajami, “Symmetric and asymmetric design and implementation of new cascaded multilevel inverter topology,” IEEE Trans. Power Electr., vol. 29, no.11, pp. 6712-6724, 2014.
23
ORIGINAL_ARTICLE
Degree of Optimality as a Measure of Distance of Power System Operation from Optimal Operation
This paper presents an algorithm based on inter-solutions of having scheduled electricity generation resources and the fuzzy logic as a sublimation tool of outcomes obtained from the schedule inter-solutions. The goal of the algorithm is to bridge the conflicts between minimal cost and other aspects of generation. In the past, the optimal scheduling of electricity generation resources has been based on the optimal activation levels of power plants over time to meet demand for the lowest cost over several time periods. At the same time, the result of that type of optimization is single-dimensional and constrained by numerous limitations. To avoid an apparently optimal solution, a new concept of optimality is presented in this paper. This concept and the associated algorithm enable one to calculate the measure of a system’s state with respect to its optimal state. The optimal system state here means that the fuzzy membership functions of the considered attributes (the characteristics of the system) have the value of one. That particular measure is called the “degree of optimality” (DOsystem). The DOsystem can be based on any of the system's attributes (economy, security, environment, etc.) that take into consideration the current and/or future state of the system. The calculation platform for the chosen electric power test system is based on one of the unit commitment solvers (in this paper, it is the genetic algorithm) and fuzzy logic as a cohesion tool of the outcomes obtained by means of the unit commitment solver. The DO-based algorithm offers the best solutions in which the attributes should not to distort each other, as is the case in a strictly deterministic nature of the Pareto optimal solution.
http://joape.uma.ac.ir/article_638_7454ca8764e9fe43eece4d0e1baadacb.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
69
79
10.22098/joape.2018.3438.1273
Economic dispatch
fuzzy logic
genetic algorithm
power system
optimality
S.
Halilčević
suad.halilcevic@untz.ba
true
1
University of Tuzla
University of Tuzla
University of Tuzla
LEAD_AUTHOR
I.
Softić
izudin.softic@untz.ba
true
2
University of Tuzla, Faculty of Electrical Engineering, Department for Power and Energy Engineering
University of Tuzla, Faculty of Electrical Engineering, Department for Power and Energy Engineering
University of Tuzla, Faculty of Electrical Engineering, Department for Power and Energy Engineering
AUTHOR
[1] G. Eichfelder, Adaptive Scalarization methods in Multiobjective Optimization, Springer, 2008, pp. 55.
1
[2] V. Chankong, and Y. Y. Haimes, Multiobjective Decision Making: Theory and Methodology, Dover Publications, Incorporated, 2008, pp. 121.
2
[3] B. Basturk, and D. Karaboga, “An artificial bee colony (ABC) algorithm for numeric function optimization,” IEEE Swarm Intelligence Symposium, 12-14, 2006, Indianapolis, Indiana, USA.
3
[4] D. C. Karia, and V. V. Godbole, “New approach for routing in mobile ad-hoc networks based on ant colony optimisation with global positioning system,” IET Networks, vol.2, no.3, pp. 171-180, 2013.
4
[5] P. M. Pardalos, D. Z. Du, and R. L. Graham, Handbook of Combinatorial Optimization, 2nd ed., Springer, 2013, pp. 89.
5
[6] J. Momoh, Electric power system applications of optimization, Marcel Dekker Inc., New York – Basel, 2001, pp. 139.
6
[7] A. Tuohy, P. Meibom, E. Denny, and M. O'Malley, “Unit commitment for systems with significant wind penetration,” IEEE Trans. Power Syst., vol. 24, pp. 592-601, 2009.
7
[8] P. A. Ruiz, C. R. Philbrick, E. Zak, K. W. Cheung, and P. W. Sauer, “Uncertainty management in the unit commitment problem,” IEEE Trans. Power Syst., vol. 24, pp. 642- 651, 2009.
8
[9] H. Shayeghi, M. Ghasemi, “FACTS devices allocation using a novel dedicated improved PSO for optimal operation of power system,” J. Oper. Autom. Power Eng., vol. 1, no. 2, pp. 124-135, 2013.
9
[10] N. Ghorbani, E. Babaei, “Combined economic dispatch and reliability in power system by using PSO-SIF algorithm,” J. Oper. Autom. Power Eng., vol. 3, no. 1, pp. 23-33, 2015.
10
[11] S. M. Mohseni-Bonab, A. Rabiee, S. Jalilzadeh, B. Mohammadi-Ivatloo, S. Nojavan, “Probabilistic multi objective optimal reactive power dispatch considering load uncertainties using monte carlo simulations” J. Oper. Autom. Power Eng., vol. 3, no. 1, pp. 83-93, 2015.
11
[12] I. G. Damousis, A. G. Bakirtzis, and P. S. Dokopoulos, “A solution to the unit-commitment problem using integer-coded genetic algorithm,” IEEE Trans. Power Syst., vol. 19, pp. 1165-1172, 2004.
12
[13] A. Viana, J. P. Pedroso, “A new MILP-based approach for unit commitment in power production planning,” Int. J. Electr. Power Energy Syst., pp. 997-1005, 2013.
13
[14] LINGO User’s guide, LINDO Systems Inc., 2011.
14
[15] J. Bracken, J. McGill, “Mathematical programs with optimization problems in the constraints,” Oper. Res., vol. 21, pp. 37-44, 1973.
15
[16] B. Colson, P. Marcotte, G. Savard, “An overview of bi-level optimization,” Ann. Oper. Res., 153:235-256, 2007.
16
[17] A. Sinha, P. Malo, and K. Deb, “Tutorial on bi-level optimization,” In Proc. Genetic Evolution. Comput. Conf., Amsterdam, Netherlands, 2013.
17
[18] D. P. Kothari, and J. Nagrath, Power System Engineering,Tata McGraw-Hill Publication, 2nd ed., 2008, pp. 124.
18
[19] S. N. Pant, and K. E. Holbert, “Fuzzy logic in decision making and signal processing,” online database, http://enpub.fulton.asu.edu/powerzone/fuzzylogic
19
[20] V. Shanthi, A. E. Jeyakumar, “Unit commitment by genetic algorithms,” Proc. IEEE PES Power Syst. Conf. Expos., 2004, vol.3, 2004, pp. 1329-1334.
20
[21] S. Halilčević, “Procedures for definition of generation ready-reserve capacity,” IEEE Trans. Power Syst., vol. 13, pp. 649-655, 1998.
21
[22] S. A. Kazarlis, A. G. Bakirtzis, and V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE Trans. Power Syst., vol.11, pp. 83-92, 1996.
22
[23] K. Iba, “Reactive power optimization by genetic algorithm,” IEEE Trans. Power Syst., vol. 9, pp. 685-692, 1994.
23
[24] J. Varela, N. Hatziargyriou, L.J. Puglisi, M. Rossi, A. Abart, and B. Bletterie, “The IGREEN grid project,” IEEE Power Energy Mag., vol. 15, pp. 30-40, 2017.
24
ORIGINAL_ARTICLE
A Novel Controller Based on Single-Phase Instantaneous p-q Power Theory for a Cascaded PWM Transformer-less STATCOM for Voltage Regulation
In this paper, dynamic performance of a transformerless cascaded PWM static synchronous shunt compensator (STATCOM) based on a novel control scheme is investigated for bus voltage regulation in a 6.6kV distribution system. The transformerless STATCOM consists of a thirteen-level cascaded H-bridge inverter, in which each voltage source H-bridge inverter should be equipped with a floating and isolated capacitor without any power source. The proposed control algorithm uses instantaneous p-q power theory in an innovative way that devotes itself not only to meet the reactive power demand but also to balance the dc link voltages at the same time. DC link voltage balancing control consists of two main parts: cluster and individual balancing. The control algorithm based on a phase shifted carrier modulation strategy has no restriction on the number of cascaded voltage source H-bridge inverters. Comprehensive simulations are presented in MATLAB/ SIMULINK environment for validating the performance of proposed transformerless STATCOM.
http://joape.uma.ac.ir/article_639_c31d4960e08ef246bd10426d6ee8ba99.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
80
88
10.22098/joape.2018.3491.1278
Cascaded H-Bridge inverter
DC link voltage balancing
p-q theory
Transformerless STATCOM
M.
Abbasi
maysamabbasi2011@gmail.com
true
1
2Department of Electrical Engineering, Urmia University, Urmia, Iran
2Department of Electrical Engineering, Urmia University, Urmia, Iran
2Department of Electrical Engineering, Urmia University, Urmia, Iran
AUTHOR
B.
Tousi
b.tousi@urmia.ac.ir
true
2
faculty of engineering, urmia university,
faculty of engineering, urmia university,
faculty of engineering, urmia university,
LEAD_AUTHOR
[1] B. Singh, V. S. Kadagala, “A new configuration of two-level 48-pulse VSCs based STATCOM for voltage regulation,” Electr. Power Syst. Res., vol. 82, no. 1, pp. 11-17, 2012.
1
[2] B. Singh, B. Singh, A. Chandra, K. Al-Haddad, “Digital implementation of an advanced static compensator for voltage profile improvement, power-factor correction and balancing of unbalanced reactive loads,” Electr. Power Syst. Res., vol. 54, no. 2, pp. 101-111, May 2000.
2
[3] N. Bigdeli, E. Ghanbaryan, K. Afshar, “Low frequency oscillations suppression via cpso based damping controller,” J. Oper. Autom. Power Eng., vol. 1, no. 2, pp. 22-32, 2013.
3
[4] H. Shayeghi, A. Ghasemi, “FACTS devices allocation using a novel dedicated improved PSO for optimal operation of power system,” J. Oper. Autom. Power Eng., vol. 1, no. 1, pp. 124-135, 2013.
4
[5] R. Kazemzadeh, M. Moazen, R. Ajabi-Farshbaf, M. Vatanpour, “STATCOM optimal allocation in transmission grids considering contingency analysis in OPF using BF-PSO algorithm,” J. Oper. Autom. Power Eng., vol. 1, no. 1, pp. 1-11, 2013.
5
[6] I. Colak, Kabalci, E., Bayindir, R., “Review of multilevel voltage source inverter topologies and control schemes,” Energy Convers. Manage., vol. 52, pp. 1114-1128, 2011.
6
[7] E. Babaei, S. Laali, M.B.B. Sharifian, “Reduction the number of power electronic devices of a cascaded multilevel inverter based on new general topology,” J. Oper. Autom. Power Eng., vol. 2, no. 2, pp. 81-90, 2014.
7
[8] M. Farhadi Kangarlu, E. Babaei, F. Blaabjerg, “An LCL-filtered single-phase multilevel inverter for grid integration of PV systems,” J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 54-65, 2016.
8
[9] H. Akagi, S. Inoue, T. Yoshii, “Control and performance of a transformerless cascade PWM STATCOM with star configuration,” IEEE Trans. Ind. Appl., vol. 43, pp. 1041-1049, 2007.
9
[10] H. Mohammadi, M. T. Bina, “A transformerless medium-voltage STATCOM topology based on extended modular multilevel converters,” IEEE Trans. Power Electron., vol. 26, pp. 1534-1545, 2011.
10
[11] H. Akagi, H. Fujita, S. Yonetani, and Y. Kondo, “A 6.6-kV transformerless STATCOM based on a five-level diode-clamped PWM converter: system design and experimentation of a 200-V 10-kVA laboratory model,” IEEE Trans. Ind. Appl., vol. 44, no. 2, pp. 672-680, 2008.
11
[12] M. Abbasi and B. Tousi, “Novel controllers based on instantaneous p-q power theory for transformerless SSSC and STATCOM,” In Proc. IEEE Int. Conf. Environ. Electr. Eng. Ind. Commer. Power Syst. Eur., Milan, Italy, 2017.
12
[13] H. Stemmler, A. Beer, H. Okayama, “Transformerless reactive series compensators with voltage source inverters,” IEEJ Trans. Ind. Appl., vol. 118, no. 10, pp. 1165–1171, 1998.
13
[14] F. Z. Peng, S. Zhang, S. Yang, D. Gunasekaran, U. Karki, “Transformerless unified power flow controller using the cascade multilevel inverter,” Proc. Int. Power Electron. Conf., Hiroshima, 2014, pp. 1342-1349.
14
[15] T. J. Hammons, “Mitigating Climate Change with Renewable and High-Efficiency Generation,” Electr. Power Compon. Syst., vol. 29, no. 9, pp. 849–865, 2001.
15
[16] P. Gopakumar, M. J. bharata Reddy, and D. kumar Mohanta, “Letter to the editor: stability concerns in smart grid with emerging renewable energy technologies,” Electr. Power Compon. Syst., vol. 42, no. 3-4, pp. 418-425, 2014.
16
[17] H. Akagi, Y. Kanazawa and A. Nabae, “Generalized theory of the instantaneous reactive power in three-phase circuits,” Proc. Int. Power Electron. Conf., Tokyo, Japan, 1983, pp. 1375-1386.
17
[18] H. Akagi, Y. Kanazawa, A. Nabae, “Instantaneous reactive power compensator comprising switching devices without energy storage components,” IEEE Trans. Ind. Appl., vol. IA-20, pp. 625-630, 1984.
18
[19] M. Y. Lada, O. Mohindo, A. Khamis, J. M. Lazi, and I. W. Jamaludin, “Simulation single phase shunt active filter based on p-q technique using MATLAB/Simulink development tools environment,” IEEE Appl. Power Electron. Colloquium, pp. 159-164 2011.
19
[20] D. Sutanto, L. A. Snider, and K. L. Mok, “EMTP simulation of a STATCOM using hysteresis current control,” In Proc. IEEE Int. Conf. Power Electron. Drive Syst., Vol. 1, pp. 531-5351999.
20
ORIGINAL_ARTICLE
Return on Investment in Transmission Network Expansion Planning Considering Wind Generation Uncertainties Applying Non-dominated Sorting Genetic Algorithm
Although significant private investment is absorbed in different sectors of power systems, transmission sector is still suffering from appropriate private investment. This is because of the pricing policies of transmission services, tariffs, and especially for investment risks. Investment risks are due to the uncertain behaviour of power systems that discourage investors to invest in the transmission sectors. In uncertain environment of power systems, a proper method is needed to find investment attractive transmission lines with high investment return and low risk. Nowadays, wind power generation has a significant portion in total generation of most power systems. However, its uncontrollable and variable nature has turned it as a main source of uncertainty in power systems. Accordingly, the wind power generation can play a fundamental role in increasing investment risk in the transmission networks. In this paper, impact of this type of generation on investment risk and returned investment cost in transmission network is investigated. With different levels of wind power penetration, the recovered values of investment cost and risk cost in transmission network are calculated and compared. This is a simple method to find investment attractive lines in presence of uncertainties. Wherein, transmission network expansion planning (TNEP) is formulated as a multi-objective optimization problem with objectives of minimizing the investment cost, maximizing the recovered investment cost and network reliability. The point estimation method (PEM) is used to address wind speed variations at wind farms sites in the optimization problem, and the NSGA II algorithm is applied to determine the trade-off regions between the TNEP objective functions. The fuzzy satisfying method is used to decide about the final optimal plan. The proposed methodology is applied on the IEEE 24-bus RTS and simplified Iran 400 kV network.
http://joape.uma.ac.ir/article_640_a8e5cba18c9ecbd6894d8f8c96f377fc.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
89
100
10.22098/joape.2018.3867.1303
Point Estimation Method
Private Investment
Transmission Network Expansion Planning
Wind power Generation
NSGA II algorithm
S.
Abbasi
shahriarabasi@gmail.com
true
1
Razi University
Razi University
Razi University
AUTHOR
H.
Abdi
hamdiabdi@razi.ac.ir
true
2
Razi University (Kermanshah)
Razi University (Kermanshah)
Razi University (Kermanshah)
LEAD_AUTHOR
[1] P. Maghouli, S. H. Hosseini, M. O. Buygi, and M. Shahidehpour, “A multi-objective framework for transmission expansion planning in deregulated environments,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 1051-1061, 2009.
1
[2] Y. Wang et al., “Pareto optimality-based multi-objective transmission planning considering transmission congestion,” Electr. Power Syst. Res., vol. 78, pp. 1619-1626, 2008.
2
[3] H. A. Gil, F. D. Galiana, and A. J. Conejo, “Multiarea transmission network cost allocation,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1293-1301, 2005.
3
[4] H. A. Gil, F. D. Galiana, E. L. Silva, “Nodal price control : a mechanism for transmission network cost allocation,” IEEE Trans. Power Syst., vol. 21, no. 1, pp. 3-10, 2006.
4
[5] J. M. Zolezzi and H. Rudnick, “Transmission cost allocation by cooperative games and coalition formation,” IEEE Trans. Power Syst., vol. 17, no. 4, pp. 1008-1015, 2002.
5
[6] M. Rahmani, R. A. Romero, M. J. Rider, and M. Rahmani, “Risk/investment-driven transmission expansion planning with multiple scenarios,” IET Gener. Transm. Distrib., vol. 7, no. 2, pp. 154-165, 2013.
6
[7] I. I. Skoteinos, G. A. Orfanos, P. S. Georgilakis, and N. D. Hatziargyriou, “Methodology for assessing transmission investments in deregulated electricity markets”, Proc. IEEE Power Tech. Conf., 2011, Trondheim, Norway, pp. 1-6, 2011.
7
[8] J. D. Molina, J. Contreras, H. Rudnick, “A risk-constrained project portfolio in centralized transmission expansion planning,” IEEE Trans. Power Syst., vol. 11, no. 3, pp. 1653-1661, 2017.
8
[9] J. Qiu, Z. Y. Dong, J. Zhao, Y. Xu, F. Luo, and J. Yang, “A risk-based approach to multi-stage probabilistic,” IEEE Trans. Power Syst., pp. 1-10, 2015.
9
[10] A. Arabali, M. Ghofrani, “A multi-objective transmission expansion planning framework in deregulated power systems with wind generation,” IEEE Trans. Power Syst., vol. 29, no. 6, pp. 3003-3011, 2014.
10
[11] H. Salazar, C. Liu, R. F. Chu, “Decision analysis of merchant transmission investment by perpetual options theory,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1194-1201, 2007.
11
[12] F. F. Wu, F. L. Zheng, and F. S. Wen, “Transmission investment and expansion planning in a restructured electricity market,” Energy, vol. 31, no. 6-7, pp. 954-966, 2006.
12
[13] M. Moeini-Aghtaie, A. Abbaspour, and M. Fotuhi-Firuzabad, “Incorporating large-scale distant wind farms in probabilistic transmission expansion Planning; Part I: Theory and Algorithm,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1594-1601, 2012.
13
[14] M. O. Buygi, G. Balzer, H. M. Shanechi, and M. Shahidehpour, “Market-based transmission expansion planning,” IEEE Trans. Power Syst., vol. 19, no. 4, pp. 2060-2067, 2004.
14
[15] “World Wind Energy Association.” [Online]. Available: http://www.wwindea.org/home/index.php.
15
[16] V. Hamidi, F. Li, and L. Yao, “Value of wind power at different locations in the grid,” IEEE Trans. Power Syst., vol. 26, no. 2, pp. 526-537, 2011.
16
[17] G. A. Orfanos, P. S. Georgilakis, and N. D. Hatziargyriou, “Transmission expansion planning of systems with increasingwind power integration,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1355-1362, 2013.
17
[18] C. Munoz, E. Sauma, J. Contreras, J. Aguado, and S. De La Torre, “Impact of high wind power penetration on transmission network expansion planning,” IET Gener. Transm. Distrib, vol. 6, no. 12, pp. 1281-1291, 2012.
18
[19] F. Ugranli and E. Karatepe, “Multi-objective transmission expansion planning considering minimization of curtailed wind energy,” Int. J. Electr. Power Energy Syst., vol. 65, pp. 348-356, 2015.
19
[20] K. Zou, A. P. Agalgaonkar, K. M. Muttaqi, and S. Perera, “Distribution system planning with incorporating DG reactive capability and system uncertainties,” IEEE Trans. Sustain. Energy, vol. 3, no. 1, pp. 112-123, 2012.
20
[21] E. Rosenblueth, “Point estimates for probability moments,” Proc. Natl. Acad. Sci., vol. 72, no. 10, pp. 3812-3814, 1975.
21
[22] G. Verbic, A. Claudio, and A. Canizares, “Probabilistic optimal power flow in electricity markets based on a two point estimate method,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1883-1894, 2006.
22
[23] S. Abbasi and H. Abdi, “Multiobjective transmission expansion planning problem based on ACOPF considering load and wind power generation uncertainties,” Int. Trans. Electr. Energy Syst, pp. 1-15, 2016.
23
[24] A. Najafi, R. Aboli, H. Falaghi, and M. Ramezani, “Capacitor placement in distorted distribution network subject to wind and load uncertainty,” J. Oper. Autom. Power Eng., vol. 4, no. 2, pp. 153-164, 2016.
24
[25] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm :,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197, 2002.
25
[26] P. K. Shukla and K. Deb, “On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods,” Eur. J. Oper. Res., vol. 181, pp. 1630-1652, 2007.
26
[27] P. Maghouli, S. H. Hosseini, M. Oloomi Buygi, and M. Shahidehpour, “A scenario-based multi-objective model for multi-stage transmission expansion planning,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 470-478, 2011.
27
[28] J. Moshtagh and S. Ghasemi, “Optimal distribution system reconfiguration using non- dominated sorting genetic algorithm ( NSGA-II ),” J. Oper. Autom. Power Eng., vol. 1, no. 1, pp. 12-21, 2013.
28
[29] J. Choi et al., “A method for transmission system expansion planning considering probabilistic reliability criteria,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1606-1615, 2005.
29
[30] S. López, A. Aguilera, and G. Blanco, “Transmission expansion planning under uncertainty: An approach based on real option and game theory against nature,” IEEE Lat. Am. Trans., vol. 11, no. 1, pp. 566-571, 2013.
30
[31] J. F. Manwell, J. G. McGowan, and A. L. Rogers, Wind energy explained: theory, design and application. John Wiley & Sons, 2010.
31
[32] R. Fang and D. J. Hill, “A new strategy for transmission expansion in competitive electricity markets,” IEEE Trans. Power Syst., vol. 18, no. 1, pp. 374-380, 2003.
32
[33] M. Shahidehpour, H. Yamin, and Z. Li, “Market operations in electric power systems : forecasting, scheduling, and risk management,” John Wiley & Sons, 2002.
33
[34] M. Moeini-Aghtaie, A. Abbaspour, and M. Fotuhi-Firuzabad, “Incorporating large-scale distant wind farms in probabilistic transmission expansion planning; part ii: case studies,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1585-1593, 2012.
34
[35] M. Sakawa and Y. Hitoshi, “An interactive fuzzy satisficing method for multiobjective nonlinear programming problems with fuzzy parameters,” Fuzzy Sets Syst., vol. 30, pp. 221-238, 1989.
35
[36] R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 12-19, 2011.
36
[37] P. M. Subcommittee, “IEEE reliability test system,” IEEE Trans. Power Appl. Syst., no. 6, pp. 2047-2054, 1979.
37
[38] “TAVANIR Annual Reports.” [Online]. Available: http://www.tavanir.org.ir.
38
[39] “Renewable Energy Organization of Iran,” 2016. [Online]. Available: http://www.suna.org.ir/fa/wind /perspective.
39
ORIGINAL_ARTICLE
Two-Stage Inverter Based on Combination of High Gain DC-DC Converter and Five-Level Inverter for PV-Battery Energy Conversion
This paper proposes a new two-stage inverter based on transformer-less high gain DC-DC converter for energy conversion of a photovoltaic system. The designed system consists of a high gain DC-DC converter cascaded with a three-phase inverter. The proposed DC-DC converter has a simple structure, and it has one switch in its structure. The output voltage of the DC-DC converter supplies DC source for the inverter part of the multi-stage inverter. The advanced two-stage inverter sample was fabricated, then the findings of the acquired simulation and hardware warranted the configuration applicability. Finally, the MATLAB/SIMULINK is employed for the simulation of PV-battery system. The obtained results revel that the proposed power conversion system effectively chases the load and generation fluctuations and also properly handles the power mismatches in PV-battery system.
http://joape.uma.ac.ir/article_641_b65778c2942a56c7fd5c19ddc3f56fef.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
101
110
10.22098/joape.2018.3958.1313
Renewable energy
Two-stage inverter
DC-DC converter
Multilevel inverter
R.
Esmaeilzadeh
rasoul_zadeh@yahoo.com
true
1
دانشجو
دانشجو
دانشجو
LEAD_AUTHOR
A.
Ajami
aajami83@yahoo.com
true
2
عضو هیات علمی
دانشگاه شهید مدنی آذربایجان
عضو هیات علمی
دانشگاه شهید مدنی آذربایجان
عضو هیات علمی
دانشگاه شهید مدنی آذربایجان
AUTHOR
M. R.
Banaei
m.banaei@azaruniv.ac.ir
true
3
استاد دانشگاه شهید مدنی آذربایجان
استاد دانشگاه شهید مدنی آذربایجان
استاد دانشگاه شهید مدنی آذربایجان
AUTHOR
[1] A. H. Einaddin, A. S. Yazdankhah, R. Kazemzadeh, “Power management in a utility connected micro-grid with multiple renewable energy sources,” J. Oper. Autom. Power Eng., vol. 5, no. 1, pp.1-9, 2017.
1
[2] K. Afshar, A. Shokri Gazafroudi, Application of stochastic programming to determine operating reserves with considering wind and load uncertainties,” J. Oper. Aut. Power Eng., vol. 1, no. 2, 96-109, 2013.
2
[3] B. S. Prasad, S. Jain, V. Agarwal, “Universal single-stage grid-connected inverter”, IEEE Trans. Energy Convers., vol. 23, no. 1, 128-137, 2008.
3
[4] E. Salary, M. R. Banaei, A. Ajami, “Multi-stage DC-AC converter based on new DC- DC converter for energy conversion,” J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 42-53, 2016.
4
[5] J. M. Shen, H. L. Jou, J. C. Wu, “Novel transformer less grid-connected power converter with negative grounding for photovoltaic generation system,” IEEE Trans. Power Electron., vol. 27, pp.1818-1829, 2012.
5
[6] X. Xiong, J. Ouyang, Modeling and transient behavior analysis of an inverter-based microgrid,” Electr. Power Company Syst., vol. 40, pp.112-130, 2012.
6
[7] M. Sarhangzadeh, S. H. Hosseini, M. B. Bannan Sharifian, G. B. Gharehpetian, “Multi input direct DC–AC converter with high-frequency link for clean power-generation systems,” IEEE Trans. Power Electron., vol. 26, no. 6, 1777-1789, 2011.
7
[8] R. Bojoi, M. Cerchio, G. Gianolio, F. Profumo, A. Tencon I, “Fuel cells for electric power generation: peculiarities and dedicated solutions for power electronic conditioning systems,” EPE J., vol. 16, no. 1, pp. 44-45, 2006.
8
[9] L.S. Yang, T.J. Liang and J.F. Chen, “Transformerless DC-DC converters with high step up voltage gain,” IEEE Trans. Ind. Electron., vol. 56, no.8, pp. 3144-3152, 2009.
9
[10] L. H. S. C. Barreto, P. P Praca, D. S. Oliveira, R. P. T. Bascope, “Single-stages topologies integrating battery charging, high voltage step-up and photovoltaic energy extraction capabilities,” IET Electron. Lett., vol. 47, no. 1, pp. 49-50, 2011.
10
[11] Z. Zhao, M. Xu, Q. Chen, J. Lai, Y. Cho, “Derivation, analysis, and implementation of a boost-buck converter-based high-efficiency PV Inverter,” IEEE Trans. Power Electron., vol. 27, pp. 1304-1313, 2012.
11
[12] L. G. Junior, M. A. G. de Brito, L. P. Sampaio, C. A. Canesin, “Single stage converters for low power stand-alone and grid-connected PV systems,” Proc. IEEE Int. Symp. Ind. Electron., pp.1112-1117, 2011.
12
[13] J. C. Rosas-Caro, J. M. Ramirez, F. Z. Peng, A. Valderrabano, “A DC/DC multilevel boost converter,” IET Proc. Power Electron., vol.3, pp.129-137, 2010.
13
[14] M. R. Banaei, E. Salary, “Two flying capacitors cascaded sub-multilevel inverter with five Switches for DC/AC conversion”, GU J. Sci., vol. 25. no. 4, pp. 875-886, 2012.
14
[15] P. Thounthong, A. Luksanasakul, P. Koseeyaporn, B. Davis, “Intelligent model-based control of a standalone photovoltaic-fuel cell power plant with super capacitor energy storage,” IEEE Trans. Sustain. Energy, vol. 4. no. 1, pp. 240-249, 2013.
15
[16] J. A. P. Lopes. C. L. Moreira and A. G Madureira, “Defining control strategies for microgrids islanded operation,” IEEE Trans. Power syst., vol. 21, no. 2, pp. 916-924, 2006.
16
[17] B. Sri Revathi, M. Prabhakar, “ Non isolated high gain DC-DC converter topologies for PV applications - A comprehensive,” Renewable Sustainable Energy Rev., vol. 66, pp. 920-933, 2016.
17
[18] L. Maharjan, S. Inoue, and H. Akagi, “A transformerless energy storage system based on a cascade multilevel PWM converter with star configuration,” IEEE Trans. Ind. Electron., vol. 44, pp. 1621-1630, 2008,
18
[19] D. Ali and D. D. Aklil-D’Halluin, “Modeling a proton exchange membrane (PEM) fuel cell system as a hybrid power supply for standalone applications,” Proc. Asia-Pac. Power Energy Eng. Conf., pp. 1-5, 2011.
19
[20] A. Tofighi and M. Kalantar, “Adaptive passivity-based control of PEM fuel cell/battery hybrid power source for stand-alone applications,” Adv. Electr. Comp. Eng., vol.10, no. 4, pp. 111-120, 2010.
20
[21] P. Palanivel and S. S. Dash, “Analysis of THD and output voltage performance for cascaded multilevel inverter using carrier pulse width modulation techniques,” IET Power Electron., vol. 4, no. 8, pp. 951-958, 2011.
21
[22] S. Laali, E. Babaei and M.B.B. Sharifian, “Reduction the number of power electronic devices of a cascaded multilevel inverter based on new general topology,” J. Oper. Autom. Power Eng., vol. 2, no. 2, pp. 81-90, 2014.
22
[23] M. T. Hough and H. Taghizadeh, “Harmonic elimination of cascade multilevel inverters with non-equal dc sources using particle swarm optimization,” IEEE Trans. Ind. Electron., vol. 57, no.11, pp. 3678-3684, 2010.
23
[24] A. Kavousi, B. Vahidi, R. Salehi, M. K. Bakhshizadeh, N. Farokhnia, and S. H. Fathi, “Application of the bee algorithm for selective harmonic elimination strategy in multilevel inverters,” IEEE Trans. Power Electron., vol. 27, no. 4, pp. 1689-1696, 2012.
24
[25] Kh. El-Naggar, T. H. Abdelhamid, “Selective harmonic elimination of new family of multilevel inverters using genetic algorithms,” Energy Convers. Manag., vol. 49, pp. 89-95, 2008.
25
[26] W. Chaa, J. Kwonb, B. Kwon, “ Highly efficient step-up dc–dc converter for photovoltaic micro-inverter,” Solar Energy, vol.135, pp. 14-21, 2016.
26
[27] M. Muthuselvi, K. Antony Samson, “Design and Analysis of PEM Fuel Cell with Multilevel Inverter Using Multicarrier PWM Techniques,” Artif. Intell. Evol. Comput. Eng. Syst., Vol. 394, pp. 1239-1252, 2016.
27
[28] J. H. Kim, Y. C. Jung, S. W. Lee, T. W. Leez, Ch. Y. Won, “Power loss analysis of interleaved soft switching boost converter for single-phase PV-PCS,” J. Power Electron., vol.10, no. 4, pp. 335-341, 2010.
28
[29] E. Babaei, S.H. Hosseini, “New cascaded multilevel inverter topology with a minimum number of switches,” Energy Convers. Manag., vol.50, pp. 2761-2767, 2009.
29
[30] A. Mahrous El-Sayed, M. Orabi, and O. M. AbdelRahim, “Two-stage micro-grid inverter with high-voltage gain for photovoltaic applications,” IET Power Electron., vol. 6, no. 9, pp. 1812-1821, 2013.
30
[31] M. Allahnoori, Sh. Kazemi, H. Abdi and R. Keyhani, “Reliability assessment of distribution systems in presence of microgrids considering uncertainty in generation and load demand,” J. Oper. Autom. Power Eng., vol. 2, no. 2, pp. 113- 120, 2014.
31
[32] E. Salary, M. R. Banaei, A. Ajami, “Design of novel step-up boost DC/DC converter,” Iran J. Sci. Technol. Trans. Electr. Eng., vol. 41, pp.13-22, 2017.
32
ORIGINAL_ARTICLE
Probabilistic Power Distribution Planning Using Multi-Objective Harmony Search Algorithm
In this paper, power distribution planning (PDP) considering distributed generators (DGs) is investigated as a dynamic multi-objective optimization problem. Moreover, Monte Carlo simulation (MCS) is applied to handle the uncertainty in electricity price and load demand. In the proposed model, investment and operation costs, losses and purchased power from the main grid are incorporated in the first objective function, while pollution emission due to DGs and the grid is considered in the second objective function. One of the important advantages of the proposed objective function is a feeder and substation expansion in addition to an optimal placement of DGs. The resulted model is a mixed-integer non-linear one, which is solved using a non-dominated sorting improved harmony search algorithm (NSIHSA). As multi-objective optimization problems do not have a unique solution, to obtain the final optimum solution, fuzzy decision making analysis tagged with planner criteria is applied. To show the effectiveness of the proposed model and its solution, it is applied to a 9-node distribution system.
http://joape.uma.ac.ir/article_642_63ddbe72ff7b225c90115d12937ae5dd.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
111
125
10.22098/joape.2018.3908.1309
Power distribution planning
Harmony search algorithm
Monte Carlo simulation
Fuzzy decision-making
A.
Rastgou
abdollah.rastgo@gmail.com
true
1
گروه مهندسی برق-دانشکده مهندسی - دانشگاه کردستان-سنندج ایران
گروه مهندسی برق-دانشکده مهندسی - دانشگاه کردستان-سنندج ایران
گروه مهندسی برق-دانشکده مهندسی - دانشگاه کردستان-سنندج ایران
AUTHOR
J.
Moshtagh
j.moshtagh@uok.ac.ir
true
2
گروه مهندسی برق- دانشکده مهندسی- دانشگاه کردستان- سنندج- ایران
گروه مهندسی برق- دانشکده مهندسی- دانشگاه کردستان- سنندج- ایران
گروه مهندسی برق- دانشکده مهندسی- دانشگاه کردستان- سنندج- ایران
LEAD_AUTHOR
S.
Bahramara
s_bahramara@yahoo.com
true
3
گروه مهندسی برق- واحد سنندج- دانشگاه آزاد اسلامی- سنندج ایران
گروه مهندسی برق- واحد سنندج- دانشگاه آزاد اسلامی- سنندج ایران
گروه مهندسی برق- واحد سنندج- دانشگاه آزاد اسلامی- سنندج ایران
AUTHOR
[1] A. Sadeghi Yazdankhah and R. Kazemzadeh, “Power management in a utility connected micro-grid with multiple renewable energy sources,” J. Oper. Autom. Power Eng., vol. 5, no. 1, pp. 1-10, 2017.
1
[2] P. S. Georgilakis and N. D. Hatziargyriou, “A review of power distribution planning in the modern power systems era: Models, methods and future research,” Electr. Power Syst. Res., vol. 121, pp. 89-100, 2015.
2
[3] M. Sadeghi and M. Kalantar, “Clean and polluting DG types planning in stochastic price conditions and DG unit uncertainties,” J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 1-15, 2016.
3
[4] M. KN and J. EA, “Optimal integration of distributed generation (DG) resources in unbalanced distribution system considering uncertainty modelling,” Int. Trans. Electr. Energy Syst., vol. 27, no. 1, 2017.
4
[5] Z. Wang, B. Chen, J. Wang, and M. M. Begovic, “Stochastic DG placement for conservation voltage reduction based on multiple replications procedure,” IEEE Trans. Power Deliv., vol. 30, no. 3, pp. 1039-1047, 2015.
5
[6] I. Kim, “Optimal distributed generation allocation for reactive power control,” IET Gener. Transm. Distrib., vol. 11, no. 6, pp. 1549-1556, 2017.
6
[7] E. Ali, S. A. Elazim, and A. Abdelaziz, “Ant lion optimization algorithm for renewable distributed generations,” Energy, vol. 116, pp. 445-458, 2016.
7
[8] M. Esmaeili, M. Sedighizadeh, and M. Esmaili, “Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using big bang-big crunch algorithm considering load uncertainty,” Energy, vol. 103, pp. 86-99, 2016.
8
[9] M. Ahmadigorji and N. Amjady, “A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorit hm,” Energy, vol. 102, pp. 199-215, 2016.
9
[10] S. Singh, T. Ghose, and S. Goswami, “Optimal feeder routing based on the bacterial foraging technique,” IEEE Trans. Power Deliv., vol. 27, no. 1, pp. 70-78, 2012.
10
[11] A. Samui, S. Singh, T. Ghose, and S. Samantaray, “A direct approach to optimal feeder routing for radial distribution system,” IEEE Trans. Power Deliv., vol. 27, no. 1, pp. 253-260, 2012.
11
[12] E. Naderi, H. Seifi, and M. S. Sepasian, “A dynamic approach for distribution system planning considering distributed generation,” IEEE Trans. Power Deliv., vol. 27, no. 3, pp. 1313-1322, 2012.
12
[13] S. M. Mazhari, H. Monsef, and H. Falaghi, “A hybrid heuristic and learning automata‐based algorithm for distribution substations siting, sizing and defining the associated service areas,” Int. Trans. Electr. Energy Syst., vol. 24, no. 3, pp. 433-456, 2014.
13
[14] S. N. Ravadanegh, N. Jahanyari, A. Amini, and N. Taghizadeghan, “Smart distribution grid multistage expansion planning under load forecasting uncertainty,” IET Gener. Transm. Distrib., vol. 10, no. 5, pp. 1136-1144, 2016.
14
[15] G. Celli, E. Ghiani, G. Soma, and F. Pilo, "Planning of reliable active distribution systems," in Proc. CIGRE, 2012, pp. 1-12.
15
[16] M. S. Nazar, M. R. Haghifam, and M. Nažar, “A scenario driven multiobjective primary–secondary distribution system expansion planning algorithm in the presence of wholesale–retail market,” Int. J. Electr. Power Energy Syst., vol. 40, no. 1, pp. 29-45, 2012.
16
[17] T.-H. Chen, E.-H. Lin, N.-C. Yang, and T.-Y. Hsieh, “Multi-objective optimization for upgrading primary feeders with distributed generators from normally closed loop to mesh arrangement,” Int J. Electr. Power Energy Syst., vol. 45, no. 1, pp. 413-419, 2013.
17
[18] I. Ziari, G. Ledwich, A. Ghosh, and G. Platt, “Optimal distribution network reinforcement considering load growth, line loss, and reliability,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 587-597, 2013.
18
[19] A. M. El-Zonkoly, “Multistage expansion planning for distribution networks including unit commitment,” IET Gener. Transm. Distrib., vol. 7, no. 7, pp. 766-778, 2013.
19
[20] M. E. Samper and A. Vargas, “Investment decisions in distribution networks under uncertainty with distributed generation—Part II: Implementation and results,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2341-2351, 2013.
20
[21] E. G. Carrano, F. G. Guimarães, R. H. Takahashi, O. M. Neto, and F. Campelo, “Electric distribution network expansion under load-evolution uncertainty using an immune system inspired algorithm,” IEEE Trans. Power Syst., vol. 22, no. 2, pp. 851-861, 2007.
21
[22] C. L. T. Borges and V. F. Martins, “Multistage expansion planning for active distribution networks under demand and distributed generation uncertainties,” Int. J. Electr. Power Energy Syst., vol. 36, no. 1, pp. 107-116, 2012.
22
[23] A. Zidan, M. F. Shaaban, and E. F. El-Saadany, “Long-term multi-objective distribution network planning by DG allocation and feeders’ reconfiguration,” Electr. Power Syst. Res., vol. 105, pp. 95-104, 2013.
23
[24] S. Favuzza, G. Graditi, M. G. Ippolito, and E. R. Sanseverino, “Optimal electrical distribution systems reinforcement planning using gas micro turbines by dynamic ant colony search algorithm,” IEEE Trans.Power Syst., vol. 22, no. 2, pp. 580-587, 2007.
24
[25] Ž. Popović, V. D. Kerleta, and D. Popović, “Hybrid simulated annealing and mixed integer linear programming algorithm for optimal planning of radial distribution networks with distributed generation,” Electr. Power Syst. Res., vol. 108, pp. 211-222, 2014.
25
[26] N. Koutsoukis, P. Georgilakis, and N. Hatziargyriou, “A Tabu search method for distribution network planning considering distributed generation and uncertainties,” Proc. Int. Conf. Probab. Methods Appl. Power Syst., 2014, pp. 1-6.
26
[27] R. Hemmati, R.-A. Hooshmand, and N. Taheri, “Distribution network expansion planning and DG placement in the presence of uncertainties,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 665-673, 2015.
27
[28] V. F. Martins and C. L. Borges, “Active distribution network integrated planning incorporating distributed generation and load response uncertainties,” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 2164-2172, 2011.
28
[29] U. Sultana, A. B. Khairuddin, A. Mokhtar, N. Zareen, and B. Sultana, “Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system,” Energy, vol. 111, pp. 525-536, 2016.
29
[30] H. Doagou-Mojarrad, G. Gharehpetian, H. Rastegar, and J. Olamaei, “Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm,” Energy, vol. 54, pp. 129-138, 2013.
30
[31] Y.-Y. Hong and S.-Y. Ho, “Determination of network configuration considering multiobjective in distribution systems using genetic algorithms,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 1062-1069, 2005.
31
[32] X.-F. Wang, Y. Song, and M. Irving, Modern power systems analysis. Springer Science & Business Media, 2010.
32
[33] Z. W. Geem, J. H. Kim, and G. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60-68, 2001.
33
[34] D. Manjarres, I. Landa-Torres, S. Gil-Lopez, J. D. Ser, M. N. Bilbao, S. S. Sanz and Z. W. Geem, “A survey on applications of the harmony search algorithm,” Engineering Applications of Artificial Intelligence, vol. 26, no. 8, pp. 1818-1831, 2013.
34
[35] M. Mahdavi, M. Fesanghary, and E. Damangir, “An improved harmony search algorithm for solving optimization problems,” Applied mathematics and computation, vol. 188, no. 2, pp. 1567-1579, 2007.
35
[36] B. Huang, B. Buckley, and T.-M. Kechadi, “Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications,” Expert Systems with Applications, vol. 37, no. 5, pp. 3638-3646, 2010.
36
[37] K. Deb, Multi-objective optimization using evolutionary algorithms. John Wiley & Sons, 2001.
37
[38] M. Haghifam, H. Falaghi, and O. Malik, “Risk-based distributed generation placement,” IET Generation Transmission and Distribution, vol. 2, no. 2, pp. 252-260, 2008.
38
[39] H. Falaghi, C. Singh, M.-R. Haghifam, and M. Ramezani, “DG integrated multistage distribution system expansion planning,” International Journal of Electrical Power & Energy Systems, vol. 33, no. 8, pp. 1489-1497, 2011.
39
[40] V. Quintana, H. Temraz, and K. Hipel, “Two-stage power system distribution planning algorithm,” IEE Proc. C (Gener., Trans. Distrib.), vol. 140, no. 1, pp. 17-29, 1993.
40
[41] H. Falaghi and M.-R. Haghifam, “ACO based algorithm for distributed generation sources allocation and sizing in distribution systems,” in Power Tech, 2007 IEEE Lausanne, 2007, pp. 555-560.
41
[42] M. Rabiee, M. Zandieh, and P. Ramezani, “Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches,” Int.l J. Produc. Res., vol. 50, no. 24, pp. 7327-7342, 2012.
42
ORIGINAL_ARTICLE
Analysis of Switched Inductor Three-level DC/DC Converter
A non-isolated DC/DC converter with high transfer gain is proposed in this paper. The presented converter consists of the switched inductor and three-level converters. The DC/DC power converter is three-level boost converter to convert the output voltage of the DC source into two voltage sources. The main advantages of DC/DC converter are using low voltage semiconductors and high gain voltage. The steady-state operation of the suggested converter is analyzed. A prototype is developed and tested to verify the performance of the proposed converter. To sum up, the MATLAB simulation results and the experimental results have transparently approved high efficiency of proposed converter as well as its feasibility.
http://joape.uma.ac.ir/article_643_d019abfc17c3557f1922c3a3857c22f5.pdf
2018-06-01T11:23:20
2018-10-23T11:23:20
126
134
10.22098/joape.2018.4507.1353
renewable energy sources
PV-Battery system
non-isolated DC/DC converter
high gain DC/DC converter
E.
Salari
salari@azaruniv.edu
true
1
Azarbaijan Shahid Madani University
Azarbaijan Shahid Madani University
Azarbaijan Shahid Madani University
AUTHOR
M. R.
Banaei
m.banaei@azaruniv.ac.ir
true
2
Azarbaijan Shahid Madani University
Azarbaijan Shahid Madani University
Azarbaijan Shahid Madani University
LEAD_AUTHOR
A.
Ajami
ajami@azaruniv.edu
true
3
Electrical Engineering Dept. of Azarbaijan Shahid Madani University
Electrical Engineering Dept. of Azarbaijan Shahid Madani University
Electrical Engineering Dept. of Azarbaijan Shahid Madani University
AUTHOR
[1] S. M. Alizadeh Shabestary, M. Saeedmanesh, A. Rahimi Kian, and E. Jalalabadi, “Real-time frequency and voltage control of an islanded mode microgrid,”J. Iran. Assoc. Electr. Electron. Eng., vol. 12, no. 3, pp. 9-14, 2015.
1
[2] E. Salary, M. R. Banaei, A. Ajami, “Design of novel step-up boost dc/dc converter, “Iran. J. Sci. Technol. Trans. Electr. Eng., vol. 41pp.13-22, 2017.
2
[3] E. Salary, M. R. Banaei, A. Ajami, “Step-up DC/DC converter based on partial power processing, “Gazi Univ. J. Sci., vol. 28, no. 4, pp.599-607, 2015.
3
[4] E. Salary, M. R. Banaei, A. Ajami, “Multi-stage DC-AC converter based on new DC-DC converter for energy conversion,” J. Oper. Autom. Power Eng., vol. 4, pp. 42-53, 2016.
4
[5] A. Asghar Ghadimi, H. Rastegar, and A. Keyhani, “Development of average model for control of a full bridge PWM DC-DC converter,” J. Iran. Assoc. Electr. Electron. Eng., vol. 4, no. 2, pp. 52-59, 2007.
5
[6] W. Li, and X. He, “Review of nonisolated high-step-up dc/dc converters in photovoltaic grid-connected applications,” IEEE Trans. Ind. Electron., vol. 58, no. 4, pp. 1239-1250, 2011.
6
[7] K. Shu-Kong, and D. D. C.Lu, “A high step-down transformerless single-stage single-switch AC/DC converter,” IEEE Trans. Power Electron., vol. 28, no. 4, pp. 36-45, 2013.
7
[8] E. H. Ismail, M. A. Al-Saffar, and A. J. Sabzali, “High conversion ratio DC–DC converters with reduced switch stress,” IEEE Trans. Circuits Syst. I, vol. 55, no. 7, pp. 2139- 2151, 2008.
8
[9] Y. Jang, and M. M. Jovanovic, “Interleaved boost converter with intrinsic voltage-doubler characteristic for universal-line PFC front end”, IEEE Trans. Power Electron., vol. 22, no. 4, pp. 1394 - 1401, 2007.
9
[10] F. S. Garcia, J. A. Pomilio, and G. Spiazzi, “Modeling and control design of the six-phase interleaved double dual boost”, Proc. 9th IEEE Int. Conf. Ind. Appl. , 2010, pp. 1-6.
10
[11] S. Choi, V. G. Agelidis, J. Yang, D. Coutellier, and P. Marabeas, “Analysis, design and experimental results of a floating-output interleaved-input boost-derived dc–dc high-gain transformer-less converter,” IET Power Electron., vol. 4, no. 1, pp. 168-180, 2011.
11
[12] F. S. Garcia, J. A. Pomilio, and G. Spiazzi, “Modeling and control design of the interleaved double dual boost converter,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3283-3290, 2013.
12
[13] H. Nomura, K. Fujiwara, and M. Yoshida, “A new DC-DC converter circuit with larger step-up/down ratio,” Proc. 37th IEEE Power Electron. Spec. Conf., pp.1 - 7, 2006.
13
[14] Y. Zhang, and and J. Liu, “Improved pulse-width modulation of diode-assisted buck-boost voltage source inverter,” IEEE Trans. Power Electron., vol. 28, no. 8, pp. 3675-3699, 2013.
14
[15] K.I. Hwu, and W.Z. Jiang, “Voltage gain enhancement for a step-up converter constructed by KY and buck-boost converters,” IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 1758-1768, 2014.
15
[16] M. R. Banaei, H. Ardi, and A. Farakhor, “Analysis and implementation of a new single-switch buck–boost DC/DC converter,” IET Power Electron., vol. 7, no. 7, pp.1906-1914, 2014.
16
[17] M. El-Sayed Ahmed, and M. Orabi, O. M. AbdelRahim, “Two-stage micro-grid inverter with high-voltage gain for photovoltaic applications,” IET Power Electron., vol. 6, no. 9, pp. 1812-1821, 2013.
17
[18] V. Yaramasu, B. Wu, M. Rivera, and J. Rodriguez, “A new power conversion system for megawatt PMSG wind turbines using four-level converters and a simple control scheme based on two-step model predictive strategy—part i: modeling and theoretical analysis,” IEEE J. Emerging Sel. Top. Power Electron., vol. 2, no. 1, pp. 3-13, 2014.
18
[19] S. Krithiga, and N. Ammasai Gounden, “Investigations of an improved PV system topology using multilevel boost converter and line commutated inverter with solutions to grid issues,” Simul. Modell. Pract. Theory, 2014, 42, pp. 147-159.
19
[20] M. F. Kangarlu, and E. Babaei, “A generalized cascaded multilevel inverter using series connection of submultilevel inverters,” IEEE Trans. Power Electron., vol. 28, no. 2, pp. 625-636, Feb. 2013.
20
[21]M. R. Banaei, E. Salary, “Application of multi-stage converter in distributed generation systems,” Energy Convers. Manage., vol. 62, pp. 76-83, 2012.
21