Iranian Journal of Electrical and Electronic Engineering

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The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experimentally that the proposed IRGA-classifier has removed two important weak points of the conventional GA-classifiers. These problems are the large number of training points and the large number of iterations to achieve a comparable performance with the Bayes classifier, which is an optimal conventional classifier. Three examples have been chosen to compare the performance of designed IRGA-classifier to conventional GA-classifier and Bayes classifier. They are the Iris data classification, the Wine data classification, and radar targets classification from backscattered signals. The results show clearly a considerable improvement for the performance of IRGA-classifier compared with a conventional GA-classifier.

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A reliable and accurate diagnosis of inter-turn short circuit faults is a challenging problem in the area of fault diagnosis of electrical machines. The purpose of this challenge is to be more efficient in fault detection and to provide a reliable method with low-cost sensors and simple numerical algorithms which not only detect the occurrence of the fault, but also locate its position in the winding. Hence, this paper presents a novel method for diagnosis of different kinds of inter-turn winding faults in a salient-pole synchronous generator using the change in the magnetic flux linkage. It describes the influence of inter-turn winding faults on the magnetic flux linkage distribution of the generator. The main feature of the proposed method is its capability to identify the faulty coils under two types of inter-turn winding faults. Also, simple algorithm, low cost sensor and sensitivity are the other feature in the proposed technique. In this method, generator air gap flux linkage is measured via search coils sensor installed under the stator wedges. Theoretical approach based on Finite Element Method (FEM) together with experimental results derived from a 4-pole, 380U, 1500 rpm, 50 Hz, 50 KVA, 3-phase salient-pole synchronous generator confirm the validity of the proposed method.

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Deregulation of power system in recent years has changed static security assessment to the major concerns for which fast and accurate evaluation methodology is needed. Contingencies related to voltage violations and power line overloading have been responsible for power system collapse. This paper presents an enhanced radial basis function neural network (RBFNN) approach for on-line ranking of the contingencies expected to cause steady state bus voltage and power flow violations. Hidden layer units (neurons) have been selected with the growing and pruning algorithm which has the superiority of being able to choose optimal unit’s center and width (radius). A feature preference technique-based class separability index and correlation coefficient has been employed to identify the relevant inputs for the neural network. The advantages of this method are simplicity of algorithm and high accuracy in classification. The effectiveness of the proposed approach has been demonstrated on IEEE 14-bus power system.

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Strategic bidding in joint energy and spinning reserve markets is a challenging task from the viewpoint of generation companies (GenCos). In this paper, the interaction between energy and spinning reserve markets is modeled considering a joint probability density function for the prices of these markets. Considering pay-as-bid pricing mechanism, the bidding problem is formulated and solved as a classic optimization problem. The results show that the contribution of a GenCo in each market strongly depends on its production cost and its level of risk-aversion. Furthermore, if reserve bid acceptance is considered subjected to winning in the energy market, it can affect the strategic bidding behavior.

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This paper presents a novel solution method for joint energy and Spinning Reserve (SR) dispatch problem. In systems in which the Lost Opportunity Cost (LOC) should be paid to generators, if the LOC is not considered in the dispatch problem, the results may differ from the truly optimum solution. Since the LOC is a non-differentiable function, including it in the formulation makes the problem solving process to be time-consuming and improper for real time applications. Here, the joint energy and SR dispatch problem considering the LOC in the objective function is reformulated as a Linear Programming (LP) problem which its solving process is computationally efficient. Also, with reliance on the performance of LP problem solving process, an iterative algorithm is proposed to overcome the self-referential difficulty arising from dependence of the LOC on the final solution. The IEEE 30-bus test system is used to examine the proposed solution method.

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In this paper, the continuous optimal control theory is used to model and solve the maximum entropy problem for a continuous random variable. The maximum entropy principle provides a method to obtain least-biased probability density function (Pdf) estimation. In this paper, to find a closed form solution for the maximum entropy problem with any number of moment constraints, the entropy is considered as a functional measure and the moment constraints are considered as the state equations. Therefore, the Pdf estimation problem can be reformulated as the optimal control problem. Finally, the proposed method is applied to estimate the Pdf of the hourly electricity prices of New England and Ontario electricity markets. Obtained results show the efficiency of the proposed method.

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The main goal of this paper is to structurally analyze impact of DSM programs on reliability indices. A new approach is presented to structurally decompose reliability index Expected Energy Not Supplied (EENS) by using Monte Carlo simulation. EENS is decomposed into two terms. The first term indicates EENS which is caused by generation contingencies. The second term indicates EENS which is caused by transmission and generation contingencies. The proposed approach can be used to indicate appropriate buses for applying DSM. Furthermore, networks are studied at two levels HLI and HLII. Studies show that in some networks reliability indices are affected mostly at the HLI level. While in some other networks, reliability indices are influenced mostly at the HLII level. It means that in these networks, reliability indices are affected by transmission contingencies. Then, it is shown that the implementation of load shifting is effective in some networks and buses. These are the ones which their EENS is more influenced by generation contingencies. However it is not effective in the ones which their EENS is more influenced by transmission contingencies. The simulation results on the IEEE-RTS and Khorasan network show the efficiency of the proposed approach.

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The main goal of this paper is to study statistical indices and evaluate AGC indices in power system which has large penetration of the WTGs. Increasing penetration of wind turbine generations, needs to study more about impacts of it on power system frequency control. Frequency control is changed with unbalancing real-time system generation and load . Also wind turbine generations have more fluctuations and make system more unbalance. Then AGC loop helps to adjust system frequency and the scheduled tie-line powers. The quality of AGC loop is measured by some indices. A good index is a proper measure shows the AGC performance just as the power system operates. One of well-known measures in literature which was introduced by NERC is Control Performance Standards(CPS). Previously it is claimed that a key factor in CPS index is related to standard deviation of generation error, installed power and frequency response. This paper focuses on impact of a several hours-ahead wind speed forecast error on this factor. Furthermore evaluation of conventional control performances in the power systems with large-scale wind turbine penetration is studied. Effects of wind speed standard deviation and also degree of wind farm penetration are analyzed and importance of mentioned factor are criticized. In addition, influence of mean wind speed forecast error on this factor is investigated. The study system is a two area system which there is significant wind farm in one of those. The results show that mean wind speed forecast error has considerable effect on AGC performance while the mentioned key factor is insensitive to this mean error.

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The main goal of this paper is to present a new day-ahead energy acquisition model for a distribution company (Disco) in a competitive electricity market environment with Interruptible Load (IL). The work formulates the Disco energy acquisition model as a bi-level optimization problem with some of real issues, and then studies and designs a Genetic Algorithm (GA) of this optimization problem too. To achieve this goal, a novel two-step procedure is proposed. At the first step, a realistic model for an industrial interruptible load is introduced, and it is shown that Interruptible load model may affect the problem modeling and solving. At the second step, Disco energy acquisition program is formulated and solved with this realistic model. As a result, this paper shows energy acquisition programming model with ILs, by considering real assumptions. The introduced method shows a good performance of problem modeling and solving algorithm both in terms of solution quality and computational results. In addition, a case study is carried out considering a test system with some assumptions. Subsequently results show the general applicability of the proposed model with potential cost saving for the Disco

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This paper develops an effective control framework for DC voltage control and power-sharing of multi-terminal DC (MTDC) grids based on an optimal power flow (OPF) procedure and the voltage-droop control. In the proposed approach, an OPF algorithm is executed at the secondary level to find optimal reference of DC voltages and active powers of all voltage-regulating converters. Then, the voltage droop characteristics of voltage-regulating converters, at the primary level, are tuned based on the OPF results such that the operating point of the MTDC grid lies on the voltage droop characteristics. Consequently, the optimally-tuned voltage droop controller leads to the optimal operation of the MTDC grid. In case of variation in load or generation of the grid, a new stable operating point is achieved based on the voltage droop characteristics. By execution of a new OPF, the voltage droop characteristics are re-tuned for optimal operation of the MTDC grid after the occurrence of the load or generation variations. The results of simulation on a grid inspired by CIGRE B4 DC grid test system demonstrate efficient grid performance under the proposed control strategy.

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The aim of the relay coordination is that protection systems detect and isolate the faulted part as fast and selective as possible. On the other hand, in order to reduce the fault clearing time, distance protection relays are usually equipped with pilot protection schemes. Such schemes can be considered in the distance and directional overcurrent relays (D&DOCRs) coordination to achieve faster protection systems, while the selectivity is maintained. Therefore, in this paper, a new formulation is presented for the relay coordination problem considering pilot protection. In the proposed formulation, the selectivity constraints for the primary distance and backup overcurrent relays are defined based on the fault at the end of the transmission lines, rather than those at the end of the first zone of the primary distance relay. To solve this nonlinear optimization problem, a combination of genetic algorithm (GA) and linear programming (LP) is used as a hybrid genetic algorithm (HGA). The proposed approach is tested on an 8-bus and the IEEE 14-bus test systems. Simulation results indicate that considering the pilot protection in the D&DOCRS coordination, not only obtains feasible and effective solutions for the relay settings, but also reduces the overall operating time of the protection system.

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Utilization of wind turbines as economic and green production units, poses new challenges to the power system planners, mainly due to the stochastic nature of the wind, adding a new source of uncertainty to the power system. Different types of distribution and correlation between this random variable and the system load makes conventional method inappropriate for modeling such a correlation. In this paper, the correlation between the wind speed and system load is modeled using Copula, a mathematical tool recently used in the field of the applied science. As the effect of the correlation coefficient is the main concern, the copula modeling technique allows simulating various scenarios with different correlations. The conducted simulations in this paper reveals that the wind speed correlation with the load has significant effect on the system reliability indices, such as expected energy not served (EENS) and loss of load probability (LOLP). Moreover, in this paper the effect of the correlation coefficient on the effective load carrying capability (ELCC) of the wind turbines is analyzed, too. To perform the aforementioned simulations and analyses, the modified RBTS with an additional wind farm is used.

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Most studies in relay coordination have focused solely on coordination of overcurrent relays while distance relays are used as the main protection of transmission lines. Since, simultaneous coordination of these two types of relays can provide a better protection, in this paper, a new approach is proposed for simultaneous coordination of distance and directional overcurrent relays (D&DOCRs). Also, pursued by most of the previously published studies, the settings of D&DOCRs are usually determined based on a main network topology which may result in mis-coordination of relays when changes occur in the network topology. In the proposed method, in order to have a robust coordination, network topology changes are taken into account in the coordination problem. In the new formulation, coordination constraints for different network topologies are added to those of the main topology. A complex nonlinear optimization problem is derived to find the desirable relay settings. Then, the problem is solved using hybridized genetic algorithm (GA) with linear programming (LP) method (HGA). The proposed method is evaluated using the IEEE 14-bus test system. According to the results, a feasible and robust solution is obtained for D&DOCRs coordination while all constraints, which are due to different network topologies, are satisfied.

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Wind energy penetration in power system has been increased very fast and large amount of capitals invested for wind farms all around the world. Meanwhile, in power systems with wind turbine generators (WTGs), the value of Available transfer capability (ATC) is influenced by the probabilistic nature of the wind power. The Mont Carlo Simulation (MCS) is the most common method to model the uncertainty of WTG. However, the MCS method suffers from low convergence rate. To overcome this shortcoming, the proposed technique in this paper uses a new formulation for solving ATC problem analytically. This lowers the computational burden of the ATC computation and hence results in increased convergence rate of the MCS. Using this fast technique to evaluate the ATC, wind generation and load correlation is required to get into modeling. A numerical method is presented to consider load and wind correlation. The proposed method is tested on the modified IEEE 118 bus to analyze the impacts of the WTGs on the ATC. The obtained results show that wind generation capacity and its correlation with system load has significant impacts on the network transfer capability. In other words, ATC probability distribution is sensitive to the wind generation capacity.

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This paper presents a novel approach for evaluating impacts of price-sensitive loads on electricity price and market power. To accomplish this aim an analytical method along with agent-based computational economics are used. At first, Nash equilibrium is achieved by computational approach of Q-learning then based on the optimal bidding strategies of GenCos, which are figured out by Q-learning, ISO's social welfare maximization is restated considering demand side bidding. In this research, it was demonstrated that Locational Marginal Price (LMP) at each node of system can be decomposed into five components. The first constitutive part is a constant value for the respective bus, while the next two components are related to GenCos and the last two parts are associated to Load Serving Entities (LSEs). Market regulators can acquire valuable information from the proposed LMP decomposition. First, sensitivity of electricity price at each bus and Lerner index of GenCos to the bidding strategies and maximum pricesensitive demand of LSEs are revealed through weighting coefficients of the last two terms in the decomposed LMP. Moreover, the decomposition of LMP expresses contribution of LSEs to the electricity price. The simulation results on two test systems confirm the capability of the proposed approach.

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In this paper, a Lyapunov-based adaptive 2^nd order sliding mode controller is proposed to control the current in an active power filter (APF). The penetration of the APFs has been exponentially increased because of their high flexibility and less resonance problems. Moreover, they can compensate high range of current harmonics and reactive power. The voltage and current control loops have always been an interesting area for researchers since the satisfactory performance of the APF is highly dependent to these control loops. Sliding mode controller (SMC) is a mighty controller when uncertain conditions are considered. However, in order to reduce the chattering- high-frequency switching- and improve the steady state operation, stability, and robustness of the controller, it is usually decided to adaptively tune the gains of the controller. In this paper, a simple-structure adaptive SMC (ASMC) is proposed which can be implemented easily. This ASMC is showed to be stable using the Lyapunov theorem and proved with SIMULINK simulation that has less steady state error, less chattering, and faster dynamic response compared to the conventional SMC.