Journal of Operation and Automation in Power Engineering
https://joape.uma.ac.ir/
Journal of Operation and Automation in Power Engineeringendaily1Sun, 01 Aug 2021 00:00:00 +0430Sun, 01 Aug 2021 00:00:00 +0430A MILP Model Incorporated With the Risk Management Tool for Self-Healing Oriented Service Restoration
https://joape.uma.ac.ir/article_1761.html
The inevitable emergence of intelligent distribution networks has introduced new features in these networks. According to most experts, self-healing is one of the main abilities of smart distribution networks. This feature increases the reliability and resiliency of networks by reacting fast and restoring the critical loads (CLs) during a fault. Nevertheless, the stochastic nature of the components in a power system imposes significant computational risk in enabling the system to self-heal. In this paper, a mathematical model is introduced for the self-healing operation of networked Microgrids (MGs) to assess the risk in the optimal service restoration (SR) problem. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) and their stochastic nature besides the distributed generation units (DGs), the ability to reconfiguration, and demand response program are considered simultaneously. The objective function is designed to maximize the restored loads and minimize the risk. The Conditional Value-at-Risk (CVaR) is used to calculate the risk of the SR as one of the most efficient and famous risk indices. In the general case study and considering $\beta $ equal to the 0, 1, 2, 3, and 4, expected values of SR for the risk-averse problem is 21.2, 20, 19.3, 19.1, and 19\% less than the risk-neutral problem, respectively. The formulation of the problem is mixed-integer linear programming (MILP), and the model is tested in the modified Civanlar test system. The analysis of several case studies has proved the performance of the proposed model and the importance of risk management in the problem.Nonsingular Terminal Sliding Mode Control for Islanded Inverter-Based Microgrids
https://joape.uma.ac.ir/article_2042.html
In high voltage direct current transmission (HVDC) system, converter transformer is an integral part of the system. Generally, core loss, copper loss and stray losses occur in the transformer. In which stray losses are produced in the transformers metallic parts such as transformer tank which can be 10% to 15% of the total loss. Experimentally, stray losses are difficult to measure. So, it is essential to use numerical modeling to predict the stray loss. The secondary winding of the converter transformer is directly linked to the rectifier or inverter. As a result, the converter transformer winding's current is non-sinusoidal. Due to non-sinusoidal current, losses are more in converter transformer than in~the power transformer. This article analyses the stray loss reduction techniques by applying wall shunt on the transformer tank surface. These stray losses are estimated for different wall shunt thickness values by varying the thickness of wall shunt using parametric analysis in 3-D finite-element analysis (FEA). Two types of wall shunts is used:-horizontal and vertical. In which horizontal wall shunt results are compared with the vertical wall shunt for non-sinusoidal and sinusoidal current excitation, where sinusoidal excitation is a fundamental component of non-sinusoidal excitation. For a case study, 315 MVA converter transformer is used to estimate stray losses on this transformer. The results obtained by the numerical method are also compared with the analytical method. Result shows that the stray losses are decreased with an increase in wall shunt thickness. Also, these losses are less for sinusoidal excitation than the non-sinusoidal excitation.Improved Power System Dynamic Stability by DFIG in the Presence of SSSC Using Adaptive Nonlinear Multi-Input Backstepping
https://joape.uma.ac.ir/article_1806.html
In this paper, Doubly Feed Induction Generator (DFIG) improves the dynamic stability of the power system in the presence of Static Synchronous Series Compensator (SSSC) using nonlinear control theory. The control method used in this study is based on the nonlinear multi-input adaptive backstepping control. The control signals are assigned to DFIG and SSSC and synchronous generator excitation system. The applied control method is more effective than the conventional linear and nonlinear ones which are reported in the literature. Also in this study, control inputs are designed considering their appropriate constraints. The controller coefficients are optimally selected using intelligent algorithms that increase the performance of the controller in terms of achieving stability. The designed control is robust against parameter variations and load changes as well as changing in the location of the disturbances. This method is simulated in two aspects of the synchronous machine model as a third-order model and a second-order one. The methods are implemented on a 39-bus IEEE system and the simulation results show the effectiveness of the proposed control mechanism.A New Transformerless DC-DC Converter for Renewable Energy Applications
https://joape.uma.ac.ir/article_1757.html
In this paper, a novel high step-up voltage switching cell formed by four passive elements and three diodes is proposed. The proposed cell can be integrated into a family of boost converters to obtain substantial dc gain as required by an electrical grid supplied such as solar or fuel cell. It is integrated into a boost converter; a new converter is obtained. The features of a new converter are significant dc gain without extreme duty cycle which enables the use of lower voltage and R${}_{Ds-on}$ MOSFET switch so as to reduce cost, the low-stress voltage on the switch and diodes, non-pulsating input current, easiness design and operation, single switch which means easiness of transistor driving, and line-load common ground. In addition, the low-voltage stress across diode allows using Schottky rectifiers to eliminate the reverse recovery current which leads to more reduction in conduction and switching losses. The equations of voltage and current in "continuous conduction mode (CCM) and discontinuous mode (DCM)" are extracted. Moreover, the voltage and current stresses on elements and switch are calculated. Finally, the performance of the proposed converter is validated by simulation results and experimental results to confirm theoretical calculation.Optimizing Multi-objective Function for User-Defined Characteristics Relays and Size of Fault Current Limiters in Radial Networks with Renewable Energy Sources
https://joape.uma.ac.ir/article_1908.html
Installing the renewable energy sources (RESs) in distribution networks changes the fault current direction, increases the fault current level and, consequently, eliminates the protection coordination between the protective devices. Overcurrent relays are among the most important protective devices in distribution networks. Proper performance of the protective system requires protection coordination between the overcurrent relays. The present paper proposes a new protection coordination scheme using digital directional overcurrent relays (DOCRs) and dual-setting digital overcurrent relays (DS-DOCRs) in the distribution network in the presence of the RESs and energy storage systems (ESSs). For this purpose, a multi-stage objective function was used. In the first stage, a weighted objective function was employed to optimize the size and location of the RESs as well as the location and impedance of the FCLs in order to reduce the loss, improve the voltage profile and decrease the variation of the feeders' currents at the connection time of the RESs. In the second stage, DOCR and DS-DOCR settings optimization, which included parameters A and B in addition to parameters I${}_{p}$ and TDS, was used to restore the lost protection coordination for the fault current in the shortest possible time. The simulation results on the IEEE 33-bus network in the presence of RESs using genetic algorithm and PSO algorithm method as well as DPL programming language in DIgSILENT software showed that the total operating time of digital relays and network loss were reduced and voltage profile was significantly improved.Robust Stochastic Blockchain Model for Peer-to-peer Energy Trading Among Charging Stations of Electric Vehicles
https://joape.uma.ac.ir/article_1900.html
Fossil-fueled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using a massive unexploited capability of the batteries in too many EVs makes these challenges opportunities. This unused capacity can be employed for the grid ancillary services and trading peer-to-peer (P2P) energy. However, the preference of EV users is one of the most important factors, which has to be considered within the scheduling process of EVs. Therefore, this paper proposes a stochastic model for EV bidirectional smart charging taking into account the preferences of EV users, P2P energy trading, and providing ancillary services of the grid. Considering the likings of EV users makes the proposed scheduling model adaptive against changing operating conditions. The presented model is formulated as an optimization problem aiming at optimal managing SOC of EV battery and electrical energy placement of several facilities considering the provision of ancillary services and contributing to P2P transactions. To evaluate the proposed model, real-world data collected from Tehran city are used as input data for simulation. Numerical results demonstrate the ability of the presented model. Simulation results display that considering the preferences of EV users in the proposed model can enhance the total income provided by the EV energy-planning model such that it could balance the charging cost. Moreover, this advanced user-based smart charging model increases P2P energy transactions amongst EVs and raises the ancillary services facility to the grid. Simulation results show that the yearly cost of optimal electrical charging on normal trips, light trips, and heavy trips is reduced by 32.6%, 51.2%, and 34.8% compared to non-optimal ones, respectively.Optimal Sizing of Distributed Power Flow Controller Based on Jellyfish Optimizer
https://joape.uma.ac.ir/article_1798.html
In the family of Flexible AC Transmission Systems (FACTS) controllers, the distributed power flow controller (DPFC) can control powerfully all the system's parameters like bus voltages magnitude, transmission angle, and line impedances with high redundancy and a wide range of compensation. In this paper, IEEE-14 bus IEEE-30 bus, and IEEE-118 bus systems are taken for the testing of the proposed approach. The optimal placement of the series and shunt converters of the DPFC is decided by the most critical bus and most critical line associated with that bus respectively. The sizing of the DPFC is decided based on the minimization of active power losses of the systems. The loss function is considered an objective function and the limits of the bus voltages magnitudes, bus voltage angles, thermal limits of the lines, and level of compensation of the DPFC are taken as the system's constraints. To solve complex problems in various fields, meta-heuristic optimizations are more popular. Among the meta-heuristic optimizers, the jellyfish optimizer is one that is based on the behavior of jellyfish in the ocean. The optimization of the objective function with constraints has been solved by time-varying acceleration coefficients (TVAC) particle swarm optimization (PSO), artificial bee colony (ABC), genetic algorithm (GA), and metaheuristic optimizer jellyfish methods. Results show that all the optimization techniques provide solutions with minimum losses. Among these methods, the solution of the jellyfish optimizer has the lowest active power losses, highest convergence rate, less number of iterations, and also takes less computational time.Comparison of Transmission Line Models by Excluding Frequency Dependence in Complex Power System for Error Estimation
https://joape.uma.ac.ir/article_1802.html
Today, commercial simulation packages can have the capability of solving complex power system networks by using various transmission line models. When there is a change in the modeling routine of transmission lines, their accuracy is also changese main aim of this paper is to compare lumped PI and distribute CP transmission line models in terms of accuracy and optimization capability. The IEEE 57 bus time domain power system models are designed by using these transmission line models for analysis in this paper. In these proposed systems the transmission line parameters are described as frequency independent. Therefore, in CP lines the Clark's transformation method does not provide exact decoupling of lines, to achieve exact decoupling of lines and accuracy the lines are continuously transposed in proposed systems. The NR load flow analysis was used for error estimation in balanced and unbalanced networks. The results had reported voltage error at the buses, transmission line error as function of line length and frequency response of line parameters. The frequency study of the line parameters was shown the PI lines system behaves as low pass filter and the CP lines system behaves as high pass filter. In this paper, also studied the optimization of proposed models by using a well-known Ant Lion Optimization (ALO) algorithm to set control variables, such as generator voltages, position of tap changing transformers and shunt capacitor banks. The optimization results of total power loss, voltage deviation and voltage stability index were compared with other algorithms. The results revealed that the ALO has best cothe nvergence characteristics and best elitism phase. Therefore, the CP lines system had shown considerable improvements of optimization results.Frequency Regulation of a Standalone Interconnected AC Microgrid Using Innovative Multistage TDF(1+FOPI) Controller
https://joape.uma.ac.ir/article_1714.html
This paper's main purpose is to offer an innovative multistage controller for load-frequency regulation of a standalone interconnected microgrid (SMG). A multistage TDF(FOPI+1) controller is designed, with the first stage being a filter built of the tilting and derivative operators. Transferring the integrator component to the second stage of the controller and employing its fractional-order (FO) form as a FO proportional-integrator (FOPI) controller results in the latter stage of the controller. To calculate the optimal controller parameters, the recently introduced Bonobo optimization algorithm (BOA) is applied. Besides, the optimization objective function is a mix of the control error signal in each area and the dynamic response characteristics of the system. In complex operating conditions such as sudden changes in power demands, uncertainties in renewable energy units' output, considering nonlinear factors, and parametric uncertainties in a two-area SMG, the performance of the proposed controller is compared with classical and multistage controllers. The results show that the TDF(1+FOPI) controller has a competent dynamic response and can be a suitable choice for performing LFC duties in SMGs. This control strategy's advantages include enhanced controller resistivity in diverse circumstances, faster reaction times, and better dynamic behavior. The results of the five studied scenarios show that using the proposed control strategy, the value of the objective function is improved by an average of more than 50% compared to other classical and conventional controllers. Similarly, improvements of more than 70% and 50% in key integral of time-weighted square error (ITSE) and Integral of absolute error (IAE) time zone indicators, respectively, are among the results of these studies.The Comparing Between Genetic Algorithm and Neural Network to Compute of Three-Basic Solar Cell Parameters with Wide Range of Measured Temperature
https://joape.uma.ac.ir/article_1924.html
Solar cell efficiency considers an important part of the PV system, the parameters (Io, IL, n, Rs, and Rsh) of solar cell is the main part that effected on efficiency. The Matlab simulation program was used to estimate the three parameters' optimization values and evaluated by the Fminsearch method, they calculated for solar cells measured from 0oC to 100oC for seven temperatures, then make comparing for the results between the Genetic Algorithm method with Neural Network Algorithm. This paper establishes the results are frequently in GA was better than NNA, with the Io being 3.0992 e-7 and IL being 3.8059 found by GA. GA is good if they have the same population size and number of iterations. The value of the objective function (fval) in GA is 0.002856 but in NNA is 0.005518. And also second objective function (fvaltemp) in GA is 0.1035 with a 0.1069 value in NNA. From the side, the execution time considers in the Fminsearch method is less than NNA and GA that being 64.9 s, 781 s, and 289 s respectively.Providing Control Method Using UPQC and Wind Turbine to Reduce Voltage Drop and Harmonics During Distribution Network Faults
https://joape.uma.ac.ir/article_1801.html
Existing generators used in renewable wind Turbines (WT) that are connected to the power system at the distribution level need a sound power grid for proper operation. The purpose of this article is to simultaneously use Unified Power Quality Conditioner (UPQC), wind turbine and appropriate control system to achieve the lowest harmonic distortion and voltage drop during network faults. Also, in this article, in order to check the efficiency of different fact tools when there is a fault in the network, a comparison between UPQC performance with static VAR compensator (SVC) and distribution synchronous static compensator (D-STATCOM) was made and the obtained results were presented. The performed simulations are based on compensation of voltage decrease and increase as well as compensation of harmonic distortion caused by nonlinear loads. The results obtained in this article show that Using UPQC in the network was able to compensate for 100% of voltage drop and voltage increase in the network, while svc and D-Statcom equipment in the best case compensated for 98\% of voltage increase and 90\% of voltage decrease. UPQC also can be the best tool to eliminate network flow harmonics. &nbsp;In the previous papers, the best value for harmonic current distortion was 1.67%, but our results showed that the harmonic distortion of the network current when using UPQC is 1.47%. Also the harmonic distortion of network current with SVC and D-Statcom is 5.67 and 4.87 percent, respectively. The capability of the equipment in compensating for short circuit fault current and protection of wind power plant is also evaluated. There was no change in wind turbine voltage during the use of UPQC and faults, and 1 P.U remained constant, but when using svc and D-Statcom equipment, the wind turbine voltage during the fault decreased by 0.3 and 0.5 P.U respectively.Adaptive Residential Energy Hubs Scheduling Considering Renewable Sources
https://joape.uma.ac.ir/article_1938.html
One of the crucial challenges within the optimal operation of smart cities is coordinated management of multiple energy carriers in the residential buildings owing to disparate and often conflicting objectives. In response to this challenge, this paper proposes a novel conceptual cost-emission-based scheme for optimal energy-gas use in a smart home in the context of residential energy hubs considering a meaningful trade-off between cost saving and environmental protection. Various energy conversion resources containing energy and heat storage systems, rooftop photovoltaic modules, and also combined heat and power units along with responsible electrical and thermal loads are taken into account in the proposed model. Furthermore, an efficient stochastic scenario-based method is executed to tackle the intense uncertainty associated with photovoltaic production. The proposed model reduces domestic energy consumption and utility costs by incorporating a weighted summation mixed objective function under various system constraints and user preferences, while at t the same time optimal task scheduling and comfort for the resident that it can guarantee a good lifestyle. The presented scheme is carried out on a realistic case study equipped with energy hubs and as expected, introduces its applicability and effectiveness in the optimal energy management of the proposed residential energy hub problem. The simulation results confirm that energy procurement costs can be saved by up to 46.16% and emission costs by 34.07% while maintaining the desired level of comfort for the head of the household.Robust Self-Scheduling of PVs-Wind-Diesel Power Generation Units in a Standalone Microgrid under Uncertain Electricity Prices
https://joape.uma.ac.ir/article_1901.html
In the semi-autonomous regions and remote islands, the multiple diesel units are usually used for supplying demand and exchanging power with other adjacent zones. In the risk-aware generation companies consisting of diesel engines, photovoltaic panels (PVs), and wind turbines, the uncertain electricity market prices affect the optimum operating points of these units, the total revenue gained from selling energy to neighbor microgrids, and the daily fuel cost of the diesel generators. Moreover, the output power of the diesel engines is a nonlinear function of their specific fuel consumption at discrete loading intervals. Therefore, this paper aims to present a risk-aware mixed integer nonlinear optimization problem for finding the best generation schedules of the diesel units involving the energy price fluctuations. The total fuel costs of the diesel engines minus the total revenue achieved from procuring power for nearby regions is minimized as a cost objective function satisfying the lower and upper generation bounds in each loading subinterval, the load-generation balance criterion, and the nominal capacities of generating units. The cubic spline interpolation is used for accurately fitting the fuel-power curves of the diesel generators at successive loading subintervals because of its zero norm of residual in comparison with 5${}^{th}$ degree and quadratic polynomials. A benchmark microgrid with six diesel generators, PVs and wind turbines is robustly scheduled using the budget of uncertainty with no need to probability distribution and membership functions of energy prices. It is revealed that this strategy is practical for each price-taker generation company, which desires the risk-aversion production patterns of the diesel power production units against the energy market price uncertainty in a specific operating horizon.Bi-level Programming of Retailer and Prosumers' Aggregator to Clear the Energy of the Day Ahead Using the Combined Method of Mixed Integer Linear Programming and Mayfly Optimization in Smart Grid
https://joape.uma.ac.ir/article_1909.html
In the restructured electricity industry, the electricity retailer, as a profit-oriented company, buys electricity from wholesale electricity markets and sells it to end customers. On the other hand, with the move of the electricity networks towards smart grids, small customers who, in addition to receiving energy from the distribution network, can generate power on a small scale, have emerged as prosumers in the electricity market environment. Therefore, the prosumers' aggregator is defined to maximize the profit of a set of prosumers in this environment. In this paper, the energy exchange between the retailer and the aggregator has been modeled as a bi-level game. At a higher level, the retailer, as a leader to maximize its profit or minimize its expenses, offers a price to buy or sell energy to the prosumers' aggregator. The aggregator also decides on the amount of exchange energy to buy or sell, to minimize the energy supply costs required of its consumers according to the retailer's bid price. In this paper, a combined method based on~MILP (Mixed Integer Linear Programming)~and MO (Mayfly Optimization) has been used to find the optimal point of this modeled game. To evaluate the efficiency of the proposed method, the three pricing methods FP (Fixed Pricing),~TOU (Time Of Using), and RTP (Real Time Pricing) as price-based demand response programs have been compared using the proposed algorithm. The simulation results show that among the three pricing methods for customers, the RTP pricing method has the highest profit for the retailer and the lowest cost for the aggregator.A Real-time Condition Monitoring-based Asset Management Model for Power Transformers in the Presence of Distributed Generation
https://joape.uma.ac.ir/article_1902.html
With the advent of advanced measurement and supervisory devices in power systems, wide area measurement systems and real-time monitoring of power systems have become viable. Accordingly, modeling techniques should be updated as well. This paper proposes a transformer asset management model based on real-time condition monitoring in the presence of distributed generation. The model is tested under different case studies and compared with the previous models in which constant failure rate model was used for asset management of transformers. The system cost includes operation, repair, and interruption costs. The objective is to determine the hourly loading of the transformer so that the cost of system is minimized. The long-term objective is to determine the loading pattern of the transformer which guaranties the most economical pattern among various options. Results showed that the proposed model is efficiently capable of returning more accurate responses if real-time monitoring data is used. A set of sensitivity analysis studies are also performed to highlight the impact of each factor separately. The contribution of distributed generators to supply the load is also investigated. Results showed that the use of distributed generators reduces the overall cost of the system by diminishing the risk-based element of the system cost.A DC-DC Converter with High Voltage Conversion Ratio Recommended for Renewable Energy Application
https://joape.uma.ac.ir/article_1903.html
In this article, a novel topology of DC-DC converter based on voltage multiplier cell and coupled inductor with higher efficiency and low blocking voltage across semiconductor is proposed for renewable energy application. The recommended topology obtains a high voltage gain using voltage multiplier cell and one coupled inductor. Only one power switch is utilized in this structure, which reduces the converter's cost. The other benefits of this converter are low number of components, high efficiency due to the zero-voltage switching and the zero-current switching of diodes, and low blocking voltage of the power switch and diodes. Besides, the voltage multiplier cell acts as a passive clamp circuit and reduces the voltage stress across the power switch. Thus, a low rated power switch can be used in the presented topology. Due to the zero-current switching in Off-state, the reverse recovery problem of diodes is reduced. To illustrate the performance and superiority of the presented topology, operation modes, steady-state and efficiency analysis, and the comparison study with other similar converters are presented. Finally, a 160~W experimental prototype with 50~kHz switching frequency and 17 V input voltage are built to confirm the theoretical investigation and effectiveness of the proposed converter.Peer-to-Peer Electricity Trading in Microgrids with \\Renewable Sources and Uncertainty Modeling Using IGDT
https://joape.uma.ac.ir/article_2043.html
Microgrids are known as the main components of energy networks because they can accommodate a large share of renewable energy sources. Peer-to-peer energy trading is one of the most effective ways to implement decentralized patterns in the electricity market. In peer-to-peer trades, each actor negotiates directly with a set of partners without any intermediaries. Peer-to-peer energy exchange methods allow direct energy exchange between producers and consumers. This study tested the peer-to-peer trading method on networks consisting of 4 microgrids. Existing microgrids have different generating sources, such as solar energy, wind turbines, and microturbines, each of which is modeled separately. Moreover, in order to reduce the uncertainty in the production of renewable sources, a battery storage system has been used in this network. Also, to encourage microgrids to use renewable resources, cut-off costs have been considered by these resources. This research uses the constrained optimization method and GAMS software with a Baron solver to optimize the problem. In the end, the uncertainty of producing renewable resources for different modes is examined using the information gap decision theory method. The available results show the power distribution between microgrids and other network components based on the objective function and existing constraints.A Modified Switched Capacitor Multilevel Inverter with Symmetric and Asymmetric Extendable Configurations
https://joape.uma.ac.ir/article_2008.html
Switched capacitor multilevel inverters with low input DC voltage sources and voltage boost capability are very attractive to producing a high voltage levels in the output. The paper introduces a modified switched capacitor multilevel inverter with voltage boost capability. The suggested topology can be extended into symmetric and asymmetric configurations. Nearest-level modulation method is employed to generate high-quality output waveforms. The presented multilevel inverter is compared with the similar configurations by considering various criteria. Finally, to confirm the operation of the suggested topology, a laboratory scale of the suggested inverter is implemented and the results are given.Frequency Stability of Hybrid Power System in the Presence of Superconducting Magnetic Energy Storage and Uncertainties
https://joape.uma.ac.ir/article_1923.html
Nowadays, in order to improve the dynamic performance of power networks and frequency control, LFC system is used in power plants. The presence of photovoltaic (PV) and wind turbine (WT) sources causes momentary changes in production and complicates the network frequency control process. In this paper, the random programming method with the Latin hypercube sampling pattern (LHS) is used to model the uncertainties of generating PV and PW sources. Also, to reduce the impact of the uncertainty of PV and PW sources on the frequency fluctuation, superconducting magnetic energy storage (SMES) has been used. Due to the fast dynamic response and favorable inertia characteristic of SMES, the performance of LFC and the stability of the system have been ameliorated. The simulation results in MATLAB software show that by step changes in the system load to the value of 0.1 pu, in the presence of SMES storage, the maximum overshoot value and the settling time of the system frequency are 16 percent and 3.2 seconds less, respectively.Investigation of Stray Losses in Converter Transformer Using Parametric Analysis of Wall Shunt Thickness
https://joape.uma.ac.ir/article_1910.html
In High Voltage Direct Current Transmission (HVDC) system, converter transformer is an integral part of the system. Generally, core loss, copper loss and stray losses occur in the transformer. &nbsp;In which stray losses are produced in the transformers metallic parts such as transformer tank which can be 10\% to 15\% of the total loss. Experimentally, stray losses are difficult to measure. So, it is essential to use numerical modelling to predict the stray loss. The secondary winding of the converter transformer is directly linked to the rectifier or inverter. As a result, the converter transformer winding's current is non-sinusoidal. Due to non-sinusoidal current, losses are more in converter transformer than in~the power transformer. This article analyses the stray loss reduction techniques by applying wall shunt on the transformer tank surface. These stray losses are estimated for different wall shunt thickness values by varying the thickness of wall shunt using parametric analysis in 3-D finite-element analysis (FEA). Two types of wall shunts is used:-horizontal and vertical. In which horizontal wall shunt results are compared with the vertical wall shunt for non-sinusoidal and sinusoidal current excitation, where sinusoidal excitation is a fundamental component of non-sinusoidal excitation. For a case study, 315 MVA converter transformer is used to estimate stray losses on this transformer. The results obtained by the numerical method are also compared with the analytical method. Result shows that the stray losses are decreased with an increase in wall shunt thickness. Also, these losses are less for sinusoidal excitation than the non-sinusoidal excitation.A Sampling Method based on System State Transition for Distribution System Adequacy Assessment using Distributed Generation
https://joape.uma.ac.ir/article_1803.html
A sampling method is proposed related to-system state transition based Monte Carlo simulation (SSTMCS) for the adequacy assessment in the radial distribution system (RDS) in the presence of distributed generation (DG) termed as a composite distribution system (CDS). This method evaluates well-being indices such as probabilities, frequency, and duration indices in healthy, marginal, and risky states. A deterministic criterion is used for adequacy assessment. Samples are generated using a load flow program for RDS used in SSTMCS. The loss sensitivity factor is utilized for the positioning of DGs in RDS. DG capacity and load at buses are considered continuous random variables. Different cases are addressed to demonstrate the impact of varying DG capacities on well-being indices. Moreover, the results are compared with the state enumeration method (SEM). IEEE-33 bus RDS is considered for this study.Torque Ripple Reduction of the Position Sensor-less Switched Reluctance Motors Applied in the Electrical Vehicles
https://joape.uma.ac.ir/article_1797.html
The Switched Reluctance Motors (SRMs) not only are low cost for industry applications, but &lrm;also they could work in various conditions with high reliability and efficiency. However, usage &lrm;of these motors in high speeds applications under discrete mode causes decreasing the &lrm;efficiency. In this paper, a new optimized control method based on the various Torque Sharing &lrm;Functions (TSFs) and optimization algorithms is proposed for Minimum Torque Ripple Point &lrm;Tracking (MTRPT) of a 4-phase SRM with 6/8 poles. In this method, turn-on and commutation angles are controlled based on the lookup table. The proposed method could adjust the rapid variations of the current in the starting mode of SRM. To show the robustness of the proposed approach, a real case study is considered, the control method is applied in an Electric Vehicle (EV) mechanism, and its performance is assessed in various motion states such as acceleration, breakage, and steady-state. Also, the position sensor for the studied EV is &lrm;neglected, which could reduce the extra costs. There are two various scenarios considered for solving the problem. First, the turn-off and turn-on angles are controlled, and the commutation angle is fixed. The results show the &lrm;robustness of the proposed method with about 90 \% diminishing the torque ripple, compared to &lrm;when all mentioned angles are fixed. In the second step, based on a lookup table, instead of using &lrm;complex analytical methods, the turn-on angle is controlled. Therefore, a variable turn-on &lrm;angle &lrm;proportional to the applied speed is applied to the commutation control system of SRM. Besides, &lrm;a lookup table is created to restrain the reduction of the turn-off angle. The simulation results are &lrm;compared to other previous methods, and the worth of the proposed method is shown.&lrm;A New Single-Phase Single-Stage Boost Inverter for Photovoltaic Applications
https://joape.uma.ac.ir/article_1792.html
In this paper, a new single-phase single-stage high step-up boost inverter appropriate for photovoltaic systems is proposed. In the proposed inverter, the duty cycle of one of the switches is adjusted to control the output voltage and increase the gain. In this structure, the dynamic model is adopted as an appropriate model to describe the low-frequency behavior of converters and extract the equations. Moreover, in this topology, a common ground is used between the input and output which can remove leakage current in different applications, including grid-connected applications. The proposed inverter is simulated using MATLAB/SIMULINK, and the experimental results are presented to verify the theoretical analysis.Probabilistic Optimal Allocation of Electric Vehicle Charging Stations Considering the Uncertain Loads by Using the Monte Carlo Simulation Method
https://joape.uma.ac.ir/article_1925.html
Nowadays, the use of electric vehicles (EVs), in the form of distributed generation, as an appropriate solution is considered to replace combustion vehicles by reducing fuel consumption and supplying needed power. In this regard, the incorporation of EVs charging stations (EVCSs) in the power network can affect the distribution networks in different ways. On the other hand, the location of EVCS in distribution networks changes operational parameters includes electrical losses, and voltage deviations. Also, the probabilistic and uncertain behaviour of the loads and their daily changes can play a significant role on power distribution networks. To this end, in this paper, first, the modelling of the EVCSs affected by the behaviour of the EVs&rsquo; owner in a power distribution network is discussed. Then, the optimal location and size of EVCSs to reduce their negative effects on the network, including network losses (active and reactive) and voltage deviations are addressed in the presence of uncertain loads. The probabilistic model is investigated based on using the Monte Carlo simulation (MCS) method. The simulation results in MATLAB software environment show a 10% increase in active and reactive power losses in most hours of the day, due to increased power flow, when EVCSs are located in the optimal placement. The power losses at 24:00-7:00. when the EVs load is very low, are reduced due to decreased power flow across the lines. The results also show that if the EVCSs are not optimally located, the voltage deviation will increase by an average of 30% over a day, while by optimal placement of EVCSs, the voltage deviation increases to a maximum of 8% of the nominal value.Optimized Cost of Energy by a Home Energy Management System Employing Dynamic Power Import Limit Strategy: A Case study Approach
https://joape.uma.ac.ir/article_1899.html
Nowadays, the centralized power system is changing to a distributed system, and various energy management systems are being installed for efficient functioning. Load side management is a vital aspect of the energy management of the power network. As residential demand is growing at a high rate, domestic customers play a crucial role in the successful implementation of demand response (DR) programs. This paper considers a single customer having a home energy management system (HEMS) for thermostatic and non-thermostatic characteristics-based appliances, photovoltaic panels, an electric vehicle, and a battery energy storage system. The effect of various DR strategies has been discussed. A mixed-integer linear programming-based model of a HEMS is modulated and solved to minimize the electricity consumption cost by employing a real-time price-based DR program using dynamic power import limits. An incentive-based DR program is considered for reducing the energy demand and maintaining the energy balance during peak hours, and peak pricing-based dynamic power import limiting DR programs are included for load shaping. The effect of load shaping on the peak to average ratio is also discussed in different scenarios. Finally, the total electricity price is calculated and analyzed by considering other test cases based on the inclusion/rejection of the mentioned DR programs.Stability Analysis of Microgrid with Passive, Active, and Dynamic Load
https://joape.uma.ac.ir/article_1793.html
The autonomous microgrid can incur a stability issue due to the low inertia offered by power electronics-based distributed generating sources of the microgrid. Due to the fast dynamics of inverters and the intermittent nature of renewables, the first phase of abrupt load change might not be shared evenly by DGs, and the system's stability deteriorates substantially. Hence the stability of the microgrid can greatly influenced by the load dynamics because of the inertialess generating sources. This paper presents a stability analysis of microgrid considering passive, active, and dynamic loads fed by inverter-based DGs. The small-signal analysis demonstrates the effect of inverter parameters and load factors. The dominance of states in oscillatory mode is examined by participation analysis. The results show that passive load does not introduce low-frequency mode, whereas rectifier interfaced active load (RIAL) introduces low-frequency mode due to DC voltage controller. The induction motor (IM) load introduces less damped eigenvalues in the microgrid and profoundly affects the real power-sharing of the system. The time-domain results verify the results obtained through eigenvalue analysis.Data Mining and SVM Based Fault Diagnostic Analysis in Modern Power System Using Time and Frequency Series Parameters Calculated From Full-Cycle Moving Window
https://joape.uma.ac.ir/article_2105.html
This paper proposes a complete diagnostic analysis of faults in a typical modern power system's transmission line using the support vector machine (SVM) with time-series parameters and frequency series parameters as features. The training and testing data of the proposed method are collected by simulating all types of faults with all possible variations on a transmission line (TL) in the IEEE-9 bus system using the PSCAD/EMTDC software. While simulating one type of fault, fault resistances and fault inception angles are also varied to account for the various behaviours of the fault. The three-phase instantaneous currents and voltages on both sides of TL are recorded at 32 samples per cycle. A thirty-two sample moving window is used to compute time-series and frequency-series parameters applied as features to the SVM. Ten-fold cross-validation is used to evaluate the performance of the proposed algorithm with evaluation metrics such as accuracy, precision, recall and F1 score. Features generation, training and testing of the proposed method, and performance comparison are done using PYTHON software. The proposed method has achieved an average accuracy of 99.996%, even in the most contaminated environment of 30 dB noise. Compared with the performance of the other popular machine learning algorithms, the proposed method has achieved more accuracy. The performance of the proposed method is also tested with different noise levels, which account for the measurement errors of 30 dB, 35 dB and 40 dB.Evaluation of Power Harvesting on DC--DC Converters to \\Extract the Maximum Power Output from TEGs Arrays under Mismatching Conditions
https://joape.uma.ac.ir/article_2121.html
Thermoelectric generators (TEGs) can transform wasted heat from industrial processes into electrical power. The power provided by TEGs systems depend on the temperature gradient, where an ideal situation for the TEGs operation is when all the modules of an array are exposed to the same temperature difference. Unfortunately, that condition is not always possible since the TEG arrays are exposed to non-uniform thermal conditions (known as mismatching). This paper proposes a novel equivalent model to represent the electrical behavior of a TEG, including a high-order approximation for the temperature dependence properties of the internal resistance and output voltage. Several configurations proposed to mitigate the mismatching phenomenon on TEGs arrays were tested, which are based on boost converters, PI controllers and the perturb and observe algorithm for maximum power point tracking: 1) TEGs serial connection with a single power converter, 2) a parallel connection where each TEG has its own converter, and 3) a serial connection where each TEG has its own converter. Those tests were performed in three temperature differences (50&deg;C,&nbsp; 100&deg;C and 180&deg;C) to study the impact of the mismatching thermal condition over the total output power. The maximum power delivered by the traditional case 1 was 10.7 W; while the output power provided by case 2 was 12.07 W (12.8 % higher) and 11.1 W (3.7 %) for case 3.A New Model for Predicting the Remaining Lifetime of \\Transformer Based on Data Obtained Using Machine Learning
https://joape.uma.ac.ir/article_2136.html
Transformers are one of the most important parts of the electric transmission and distribution networks, and their performance directly affects the reliability and stability of the grid. Maintenance and replacing the faulted transformers could be time-consuming and costly and accordingly, a solution should be proposed to prevent it. This led to studies in the field of transformer lifetime management. As a result, estimating the remaining lifetime of the transformer is a crucial part for the mentioned solution. Therefore, this paper aims to tackle this issue through employing a new algorithm to estimate the lifetime of a transformer by combining selection methods and Artificial Intelligence (AI)-based techniques. The main goal of this method is to reduce the estimation error and estimation time simultaneously. The proposed approach assesses transformers based on environmental conditions, power quality, oil quality, and dissolved gas analysis (DGA). Consideration of additional factors overcomes the disadvantage of traditional methods and gives a meticulous result. In this respect, the collected data from the power transformer of Iran and Iraq as well as regions with different conditions are employed in the studied algorithm. Several combinations of algorithms are investigated to choose the best one. Principal Component Analysis (PCA) is employed in the next step for weighing the various parameters to improve the accuracy and decrease execution time. Results show that the Bayesian neural network provides the best performance in the predicting remaining lifetime of the transformer with an accuracy about 98.4%.