Research paper
H. Bahlouli; A. Mansouri; M. Bouhamida
Abstract
The high cost and complexity of using sensors for controlling processes have led to the development of observer techniques that aim to estimate system states without the need for sensors. These techniques reduce system complexity and can potentially reduce product and maintenance costs. In this paper, ...
Read More
The high cost and complexity of using sensors for controlling processes have led to the development of observer techniques that aim to estimate system states without the need for sensors. These techniques reduce system complexity and can potentially reduce product and maintenance costs. In this paper, we present an interconnected high gain observer (IHGO) that estimates the electromagnetic torque, speed, and position of a doubly fed induction generator-based wind turbine (DFIG-WT) using only voltage, current, and wind speed measurements. The IHGO is designed to be robust to parameter uncertainties and its stability is assessed using Lyapunov theory. To guarantee finite time convergence, a Super Twisting-based High Order Sliding Mode (ST-HOSM) controller is used for direct torque control. The ST-HOSM is a simple algorithm that maintains the sliding mode characteristics, provides robustness against disturbance, and reduces the chattering phenomenon. The controller and observer are designed in the $\alpha\beta$ frame to avoid the use of a phase-locked loop (PLL). Simulation results confirmed the effectiveness of the proposed control strategy under parameter uncertainties, power and speed variations, grid voltage dip and current sensor noise.
Research paper
F. Sedaghati; S. Ebrahimzadeh; H. Dolati; H. Shayeghi
Abstract
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 ...
Read More
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.
Research paper
M.R. Negahdari; A. Ghaedi; M. Nafar; M. Simab
Abstract
For providing required load in n coastal and island regions, tidal barrage can be integrated in microgrids. To produce electricity from tides, in tidal barrage, water is moved between sea and reservoir through sluices containing turbines to generate electricity. In operation phase, produced power of ...
Read More
For providing required load in n coastal and island regions, tidal barrage can be integrated in microgrids. To produce electricity from tides, in tidal barrage, water is moved between sea and reservoir through sluices containing turbines to generate electricity. In operation phase, produced power of tidal barrages depends on number of turbines, sluices and hydro-pumps. Thus, to maximize generated energy of tidal barrage, optimum number of turbines, sluices and hydro-pumps can be obtained through heuristic optimization techniques. Because of tidal level variation, generated power of tidal barrages changes over time. Thus, for load supplying, other renewable resources such as photovoltaic units, batteries, fuel-based generation units and grid-connected mode of microgrid are utilized. In this research, two-stage optimal operation of microgrids composed of tidal barrage, photovoltaic units, batteries and fuel-based generation units is done. In first stage, optimum number of turbines, sluices and hydro-pumps related to tidal barrage is determined for maximizing produced energy of tidal unit during time horizon of the study. In second stage, remaining load of microgrid is provided by photovoltaic units, batteries, fuel-based generation units and main network. To this end, generated power of fuel-based plants and power exchanged between microgrid and main grid are determined for minimizing operating cost of microgrid. The operating cost including operating cost of fuel-based generation units, cost of exchanged power between main grid and microgrid and penalties of load curtailment is optimized using particle swarm optimization method. Numerical results presents among different optimization algorithms, particle swarm method has performed best in operation studies of tidal barrage. For understudied microgrid, maximum generated energy of tidal barrage is 25.052 MWh, and minimum operating cost of the microgrid is 39868 $.
Research paper
S. Behzadi; A. Bagheri; A. Rabiee
Abstract
Due to the increasing occurrence of natural disasters, importance of maintaining sustainable energy for cities and society is felt more than ever. On the other hand, power loss reduction is a challenging issue of active distribution networks (ADNs). Therefore, the distribution network operators (DNOs) ...
Read More
Due to the increasing occurrence of natural disasters, importance of maintaining sustainable energy for cities and society is felt more than ever. On the other hand, power loss reduction is a challenging issue of active distribution networks (ADNs). Therefore, the distribution network operators (DNOs) should have a certain view on these two problems in today’s smart grids. In this paper, a new convex optimization model is proposed with two objective functions including energy loss reduction in normal operating mode and system load shedding minimization in critical conditions after the occurrence of natural disasters. This purpose is fulfilled through optimal allocation of distributed generation (DG) units from both conventional and renewable types as well as energy storage systems (ESSs). In addition, a new formulation has been derived to form optimal micro-grids (MGs) aiming at energy loss reduction in normal operating condition and resiliency index improvement under emergency situations. The developed model is implemented in GAMS software and the studies have been tested and analyzed on the IEEE 33-bus system. The results verify the effectiveness of the proposed method in terms of energy loss reduction as well as resilience enhancement in extreme operation condition following severe disruptions in the system.
Research paper
Relays & Protection
O. koduri; R. Ramachandran; M. Saiveerraju
Abstract
This paper presents two intelligent classifier schemes for classifying the faults in a series capacitor compensated transmission line (SCCTL). The first proposed intelligent classifier scheme is a particle swarm optimization-assisted artificial neural network (PSO-ANN). The second, proposed one is a ...
Read More
This paper presents two intelligent classifier schemes for classifying the faults in a series capacitor compensated transmission line (SCCTL). The first proposed intelligent classifier scheme is a particle swarm optimization-assisted artificial neural network (PSO-ANN). The second, proposed one is a teaching-learning optimization-assisted artificial neural network (TLBO-ANN). For each type of fault, the 3-phase current signals are acquired at the sending end and processed through empirical mode decomposition (EMD), to decompose into six intrinsic mode functions. The neighborhood component analysis is used to extract the best feature intrinsic mode functions. From the identified best feature intrinsic mode functions, the energy of each phase of the line is computed. The energy of each phase is fed as inputs for both PSO-ANN and TLBO-ANN classifiers. The practicability of the proposed intelligent classifier schemes has been tested on a 500$\,kV$, 50$\,Hz$, and 300$\,km$ long line with a midpoint series capacitor using MATLAB/Simulink Software. The results demonstrate that the classifier schemes are able to accurately classify faults in less than a half-cycle. Furthermore, the efficacy of the proposed intelligent classifier schemes has been evaluated using Performance Indices including Kappa Statistics, Mean Absolute Error, Root Mean Square Error, Precision, Recall, F-measure, and Receiver Operating Characteristics. From the results of Performance Indices, it is concluded that the proposed TLBO-based artificial neural network classifier outperforms the PSO-based artificial neural network classifier. Finally, the efficacies of proposed intelligent classifier schemes are compared to existing approaches.
Research paper
M. Zadehbagheri; M.J. Kiani; S. Khandan
Abstract
Nowadays micro-grids (MG) as one of the most important methods used for electric power generation from renewable energy to reduce dependence on fossil fuels and reducing environmental pollution have been considered. Due to the increasing number of distributed generation (DG) sources and MGs in the power ...
Read More
Nowadays micro-grids (MG) as one of the most important methods used for electric power generation from renewable energy to reduce dependence on fossil fuels and reducing environmental pollution have been considered. Due to the increasing number of distributed generation (DG) sources and MGs in the power grids, it is of particular importance to design and implement a suitable controller in order to use all the available capacities in these systems. The uncertainty in prediction of power generation can be considered as disturbances into the electrical system, making it difficult to control, and eventually resulting in an unstable system. With the use of power electronic converters the power and voltage of MG can be controlled. In this paper, a 13-bus MG is proposed. This MG includes 3 wind farms and 2 PV farms. A robust sliding mode controller (SMC) is used to control voltage source converters of PV farms. A load shedding program is proposed to avoid complete blackout of MG in case of islanding that recover MG voltage to normal range after a voltage collapse. Simulations were performed using MATLAB/SIMULINK software on a 13-bus IEEE micro grid, and the effectiveness of the proposed control and operational method was investigated and confirmed.
Research paper
Energy Management
Sh. Shadi; J. Salehi; A. Safari
Abstract
Energy management (EM) in smart distribution networks (SDN) is to schedule the power transaction between the SDN and the existing distributed energy resources (DERs) e.g., distributed generations, especially renewable resources and electrical vehicles, from an eco-technical viewpoint. Due to the dual ...
Read More
Energy management (EM) in smart distribution networks (SDN) is to schedule the power transaction between the SDN and the existing distributed energy resources (DERs) e.g., distributed generations, especially renewable resources and electrical vehicles, from an eco-technical viewpoint. Due to the dual role of electric vehicles (EVs) acting as a power source and load, they presented both challenges and opportunities in EM. The complexity of EM increases as DERs become more prevalent in SDN. Moreover, the uncertainties of renewable resources, price, and load besides the uncertainties related to the place, amount, and time of EV’s charging makes EM a more intricate field. This supports the necessity of extensive tools and approaches to manage EM in SDNs. In this respect, this paper proposes an optimum scenario-based stochastic energy management scheme for intelligent distribution networks. The proposed approach is modeled as a MINLP problem and solved in GAMS software under the DICOPT solver. The test is conducted on a 33-bus SDN with and without factoring in uncertainties.
Research paper
M. Karimi; M. Eslamian
Abstract
This paper presents a resilience-based approach for critical load restoration in distribution networks using microgrids during extreme events when the main supply is disrupted. Reconfiguration of the distribution network using graph theory is investigated, for which Dijkstra's algorithm is first used ...
Read More
This paper presents a resilience-based approach for critical load restoration in distribution networks using microgrids during extreme events when the main supply is disrupted. Reconfiguration of the distribution network using graph theory is investigated, for which Dijkstra's algorithm is first used to determine the shortest paths between microgrids and critical loads, and then the feasible restoration trees are established by combining the restorable paths. A mixed-integer linear programming (MILP) model is then used to find the optimal selection of feasible restoration trees to make a restoration scheme. The service restoration is implemented with the objectives of maximizing the energy delivered to the critical loads and minimizing the number of switching operations. The limited fuel storage of the generation sources in microgrids, the operational constraints of the network and microgrids, as well as the radiality constraint of the restored sub-networks, are considered the constraints of the optimization problem. The presented method can be used for optimal restoration of critical loads including the number of switching operations which is essential for the ease of implementation of a restoration plan. The results of simulations on a 118-bus distribution network demonstrate the efficiency of the procedure.
