[1] S. Panda, "Multi-objective evolutionary algorithm for SSSC-based controller design," Electric Power Systems Research, vol. 79, no. 6, pp. 937-944, June2009.
Abstract: In this paper, an evolutionary multi-objective optimization approach is employed to design a static synchronous series compensator (SSSC)-based controller. The design objective is to improve the transient performance of a power system subjected to a severe disturbance by damping the multi-modal oscillations namely; local mode, inter-area mode and inter-plant mode. A genetic algorithm (GA)-based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented and compared with a PI controller under various disturbances namely; three-phase fault, line outage, loss of load and unbalanced faults to show the effectiveness and robustness of the proposed approach.
Abstract: In this paper, an evolutionary multi-objective optimization approach is employed to design a static synchronous series compensator (SSSC)-based controller. The design objective is to improve the transient performance of a power system subjected to a severe disturbance by damping the multi-modal oscillations namely; local mode, inter-area mode and inter-plant mode. A genetic algorithm (GA)-based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented and compared with a PI controller under various disturbances namely; three-phase fault, line outage, loss of load and unbalanced faults to show the effectiveness and robustness of the proposed approach.
[2] K. Sebaa and M. Boudour, "Optimal locations and tuning of robust power system stabilizer using genetic algorithms," Electric Power Systems Research, vol. 79, no. 2, pp. 406-416, Feb.2009.
Abstract: Optimal locations and design of robust multimachine power system stabilizers (PSSs) using genetic algorithms (GA) is presented in this paper. The PSS parameters and locations are computed to assure maximum damping performance under different operating conditions. The efficacy of this technique in damping local and inter-area modes of oscillations in multimachine power systems is confirmed through nonlinear simulation results and eigenvalues analysis.
Abstract: Optimal locations and design of robust multimachine power system stabilizers (PSSs) using genetic algorithms (GA) is presented in this paper. The PSS parameters and locations are computed to assure maximum damping performance under different operating conditions. The efficacy of this technique in damping local and inter-area modes of oscillations in multimachine power systems is confirmed through nonlinear simulation results and eigenvalues analysis.
[3] M. H. Ali, T. Murata, and J. Tamura, "Transient stability enhancement by fuzzy logic-controlled SMES considering coordination with optimal reclosing of circuit breakers," Ieee Transactions on Power Systems, vol. 23, no. 2, pp. 631-640, May2008.
Abstract: This paper aims at investigating the effect of the coordination of fuzzy logic-controlled superconducting magnetic energy storage (SMES) and optimal reclosing on the transient stability in a simulated multi-machine power system during unsuccessful reclosing of circuit breakers. The performance of the coordinated operation of fuzzy logic-controlled SMES and optimal reclosing is compared to that of the coordinated operation of fuzzy logic-controlled SMES and conventional auto-reclosing. Also, the performance of the fuzzy logic-controlled SMES is compared to that of an alternative static nonlinear controlled SMES. The control scheme of SMES is based on a pulse width modulation (PWM) voltage source converter (VSC) and a two-quadrant dc-dc chopper using gate-turn-off (GTO) thyristor. The parameters of the proposed fuzzy logic controller are optimally tuned by the genetic algorithm (GA) method. Simulation results of both balanced and unbalanced faults at different points in a multi-machine power system show that the coordinated operation of fuzzy controlled SMES and optimal reclosing is able to stabilize the system well in case of an unsuccessful reclosing. Moreover, the transient stability performance of the coordinated operation of fuzzy controlled SMES and optimal reclosing is better than that of the coordinated operation of fuzzy controlled SMES and conventional auto-reclosing. Also, the performance of the fuzzy logic-controlled SMES is better than that of the static nonlinear controlled SMES
[4] S. Mishra, M. Tripathy, and J. Nanda, "Multi-machine power system stabilizer design by rule based bacteria foraging," Electric Power Systems Research, vol. 77, no. 12, pp. 1595-1607, Oct.2007.
Abstract: Several power system stabilizers (PSS) connected in number of machines in a multi-machine power systems, pose the problem of appropriate tuning of their parameters so that overall system dynamic stability can be improved in a robust way. Based on the foraging behavior of Escherichia coli bacteria in human intestine, this paper attempts to optimize simultaneously three constants each of several PSS present in a multi-machine power system. The tuning is done taking an objective function that incorporates a multi-operative condition, consisting of nominal and various changed conditions, into it. The convergence with the proposed rule based bacteria foraging (RBBF) optimization technique is superior to the conventional and genetic algorithm (GA) techniques. Robustness of tuning with the proposed method was verified, with transient stability analysis of the system by time domain simulations subjecting the power system to different types of disturbances.
Abstract: Several power system stabilizers (PSS) connected in number of machines in a multi-machine power systems, pose the problem of appropriate tuning of their parameters so that overall system dynamic stability can be improved in a robust way. Based on the foraging behavior of Escherichia coli bacteria in human intestine, this paper attempts to optimize simultaneously three constants each of several PSS present in a multi-machine power system. The tuning is done taking an objective function that incorporates a multi-operative condition, consisting of nominal and various changed conditions, into it. The convergence with the proposed rule based bacteria foraging (RBBF) optimization technique is superior to the conventional and genetic algorithm (GA) techniques. Robustness of tuning with the proposed method was verified, with transient stability analysis of the system by time domain simulations subjecting the power system to different types of disturbances.
[5] S. Panda and R. N. Patel, "Optimal location of shunt FACTS controllers for transient stability improvement employing genetic algorithm," Electric Power Components and Systems, vol. 35, no. 2, pp. 189-203, Feb.2007.
Abstract: This article deals with determining the optimal location of a shunt flexible AC transmission systems (FACTS) device for a long transmission line with predefined direction of real power flow, so that maximum improvement in its transient stability performance is achieved. The location of a shunt FACTS controllers is formulated as an optimization problem, and genetic algorithm (GA) is employed to search for the optimal off-center location of static synchronous compensator (STATCOM) for different operating conditions to maintain transient stability. A two-area test system is used to show the effectiveness of the proposed method for determining the optimal location. The results show that, by changing the location of a STATCOM, transient stability can be improved. It is also observed that the optimal location depends on the line loading and initial operating conditions of the system
Abstract: This article deals with determining the optimal location of a shunt flexible AC transmission systems (FACTS) device for a long transmission line with predefined direction of real power flow, so that maximum improvement in its transient stability performance is achieved. The location of a shunt FACTS controllers is formulated as an optimization problem, and genetic algorithm (GA) is employed to search for the optimal off-center location of static synchronous compensator (STATCOM) for different operating conditions to maintain transient stability. A two-area test system is used to show the effectiveness of the proposed method for determining the optimal location. The results show that, by changing the location of a STATCOM, transient stability can be improved. It is also observed that the optimal location depends on the line loading and initial operating conditions of the system
[6] T. Senjyu, Y. Morishima, T. Yamashita, K. Uezato, and H. Fujita, "Recurrent neural network supplementary stabilization controller for automatic voltage regulator and governor," Electric Power Components and Systems, vol. 31, no. 7, pp. 693-707, July2003.
Abstract: Excitation controllers such as automatic voltage regulators (AVRs) and power system stabilizers (PSSs) are normally installed on synchronous generators for improving electric power systems' transient stability. The PSS optimized by the genetic algorithm (GA) has a certain robustness. However, since the power system is nonlinear, drastic changes in the system caused by faults and circuit switching may cause control performance to become unsatisfactory. Then a method using a nonlinear neural network can be used to tune the control systems. This method of using neural networks has been reported in recent years. This paper presents a recurrent neural network (RNN) stabilization controller to improve the transient stability of power systems. The proposed controller is constructed by a three-layer (8-9-1) RNN, of which inputs are DeltaP(e) and Deltaomega. The weights of the proposed controller are adjusted online to maintain electrical output power deviation equal to zero. By applying the proposed controller, good damping characteristics over a wide range of operating conditions can be realized. The ability of the proposed controller has been investigated in a single-machine infinite-bus system
Abstract: Excitation controllers such as automatic voltage regulators (AVRs) and power system stabilizers (PSSs) are normally installed on synchronous generators for improving electric power systems' transient stability. The PSS optimized by the genetic algorithm (GA) has a certain robustness. However, since the power system is nonlinear, drastic changes in the system caused by faults and circuit switching may cause control performance to become unsatisfactory. Then a method using a nonlinear neural network can be used to tune the control systems. This method of using neural networks has been reported in recent years. This paper presents a recurrent neural network (RNN) stabilization controller to improve the transient stability of power systems. The proposed controller is constructed by a three-layer (8-9-1) RNN, of which inputs are DeltaP(e) and Deltaomega. The weights of the proposed controller are adjusted online to maintain electrical output power deviation equal to zero. By applying the proposed controller, good damping characteristics over a wide range of operating conditions can be realized. The ability of the proposed controller has been investigated in a single-machine infinite-bus system
[7] T. Senjyu, Y. Morishima, T. Arakaki, and K. Uezato, "Improvement of multimachine power system stability using adaptive PSS," Electric Power Components and Systems, vol. 30, no. 4, pp. 361-375, Apr.2002.
Abstract: The excitation controllers, such as automatic voltage regulator (AVR) and power system stabilizer (PSS) for improving electric power systems transient stability, have been installed on synchronous generators. A design procedure is shown for the controller parameter-tuning applying a genetic algorithm (GA). The PSS optimized by GA has a certain robustness; however, since the power system is nonlinear, drastic changes in the system caused by faults and circuit switching may cause control performance to become unsatisfactory. Then, a method using a nonlinear neural network can be used to tune the control Systems. This method of using neural networks has been reported in recent years. This paper presents an adaptive power system stabilizer (APSS) based on a recurrent neural network (RNN) to enhance the dynamic stability of a power system. The proposed APSS is applied in parallel with a conventional PSS (CPSS) to enhance the performance of power system stability. Both the APSS and CPSS is used for stabilizing signals. The APSS is constructed by a three-layered (8-9-1) RNN, of which inputs are DeltaP(e) and Deltaomega. The weights of APSS are adjusted online to maintain electrical output power deviation to zero. By applying the proposed APSS, good damping characteristics over a wide range of operating conditions can be realized. The ability of the proposed APSS has been investigated in a three-machine power system
Abstract: The excitation controllers, such as automatic voltage regulator (AVR) and power system stabilizer (PSS) for improving electric power systems transient stability, have been installed on synchronous generators. A design procedure is shown for the controller parameter-tuning applying a genetic algorithm (GA). The PSS optimized by GA has a certain robustness; however, since the power system is nonlinear, drastic changes in the system caused by faults and circuit switching may cause control performance to become unsatisfactory. Then, a method using a nonlinear neural network can be used to tune the control Systems. This method of using neural networks has been reported in recent years. This paper presents an adaptive power system stabilizer (APSS) based on a recurrent neural network (RNN) to enhance the dynamic stability of a power system. The proposed APSS is applied in parallel with a conventional PSS (CPSS) to enhance the performance of power system stability. Both the APSS and CPSS is used for stabilizing signals. The APSS is constructed by a three-layered (8-9-1) RNN, of which inputs are DeltaP(e) and Deltaomega. The weights of APSS are adjusted online to maintain electrical output power deviation to zero. By applying the proposed APSS, good damping characteristics over a wide range of operating conditions can be realized. The ability of the proposed APSS has been investigated in a three-machine power system
[8] T. Senjyu, A. Miyazato, and K. Uezato, "Enhancement of transient stability of multi-machine power systems by using fuzzy-genetic controller," Journal of Intelligent & Fuzzy Systems, vol. 8, no. 1, pp. 19-26, 2000.
Abstract: This article presents the procedure to design a cooperative fuzzy controller applying a genetic algorithm (GA) to improve power system transient stability. A remarkable feature of the method lies in the fact that it can quickly provide a global approximately optimal solution for gains and parameters for the fuzzy controller. In real application problems, trial and error method is extensively used to decide the gains and parameters. However, when the system condition or fault location changes, it is necessary to iterate again to obtain an approximately optimal controller. Moreover, if parameters are changed, it is difficult to achieve good dynamic response. Hence, a cooperative controller using a fuzzy-genetic system seems a quite efficient means for controlling electric generators
Abstract: This article presents the procedure to design a cooperative fuzzy controller applying a genetic algorithm (GA) to improve power system transient stability. A remarkable feature of the method lies in the fact that it can quickly provide a global approximately optimal solution for gains and parameters for the fuzzy controller. In real application problems, trial and error method is extensively used to decide the gains and parameters. However, when the system condition or fault location changes, it is necessary to iterate again to obtain an approximately optimal controller. Moreover, if parameters are changed, it is difficult to achieve good dynamic response. Hence, a cooperative controller using a fuzzy-genetic system seems a quite efficient means for controlling electric generators
[9] M. A. Abido, "Coordinated design of power system stabilizers and static phase shifters using genetic algorithm," Electric Machines and Power Systems, vol. 27, no. 10, pp. 1069-1084, Oct.1999.
Abstract: Coordinated design of a power system stabilizer (PSS) and a static phase shifter (SPS) using genetic algorithm (GA) is investigated in, this paper. The design problem of PSS and SPS controller is formulated as an optimization problem. An eigenvalue-based objective function to increase the system damping is proposed. Then, GA is employed to search for optimal controller parameters. Different control schemes have been proposed and tested on a weakly connected power system with different disturbances, loading conditions, and parameter variations. It was observed that although the PSS enhances the power system stability, the SPS controller provides most of the damping and improves the voltage profile of the system. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and system parameter variations
Abstract: Coordinated design of a power system stabilizer (PSS) and a static phase shifter (SPS) using genetic algorithm (GA) is investigated in, this paper. The design problem of PSS and SPS controller is formulated as an optimization problem. An eigenvalue-based objective function to increase the system damping is proposed. Then, GA is employed to search for optimal controller parameters. Different control schemes have been proposed and tested on a weakly connected power system with different disturbances, loading conditions, and parameter variations. It was observed that although the PSS enhances the power system stability, the SPS controller provides most of the damping and improves the voltage profile of the system. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and system parameter variations
[10] W. A. Farag, V. H. Quintana, and G. Lambert-Torres, "An optimized fuzzy controller for a synchronous generator in a multi-machine environment," Fuzzy Sets and Systems, vol. 102, no. 1, pp. 71-84, Feb.1999.
Abstract: In this paper, an optimized neuro-fuzzy power-system stabilizer (NF PSS) is proposed to improve the transient and dynamic stability of synchronous machines. The NF PSS employs a five-layer fuzzy-neural network (FNN). The learning scheme of this FNN is composed of three phases. The first phase uses a clustering algorithm for coarse identification of the initial membership functions of the fuzzy controller (FC). The second phase extracts the linguistic-fuzzy rules from the available training data. In the third phase, a multi-resolutional dynamic genetic algorithm (MRD-GA) is used to fine-tune and optimize the membership functions of the FC. Extensive simulation studies have been carried out to show the performance of the NF PSS and to compare it with a Conventional PSS (CPSS) in a multi-machine power-system environment. (C) 1999 Published by Elsevier Science B.V. All rights reserved
Abstract: In this paper, an optimized neuro-fuzzy power-system stabilizer (NF PSS) is proposed to improve the transient and dynamic stability of synchronous machines. The NF PSS employs a five-layer fuzzy-neural network (FNN). The learning scheme of this FNN is composed of three phases. The first phase uses a clustering algorithm for coarse identification of the initial membership functions of the fuzzy controller (FC). The second phase extracts the linguistic-fuzzy rules from the available training data. In the third phase, a multi-resolutional dynamic genetic algorithm (MRD-GA) is used to fine-tune and optimize the membership functions of the FC. Extensive simulation studies have been carried out to show the performance of the NF PSS and to compare it with a Conventional PSS (CPSS) in a multi-machine power-system environment. (C) 1999 Published by Elsevier Science B.V. All rights reserved
[11] H. Saitoh, Y. Takano, and J. Toyoda, "Genetic algorithm-based method for contingency screening in power systems," Electrical Engineering in Japan, vol. 116, no. 2, pp. 99-111, Feb.1996.
Abstract: This paper proposes an application of genetic algorithm (GA) to contingency screening in power systems. The contingency selected by the GA-based screening method is the double line outage which has the risk of causing transient instability. Generally, the contingency screening problem including multiple outage can be interpreted as the combinatorial optimization one for searching the combination of single outages which makes the system insecure. Therefore, GA which is one of the probabilistic searches for combinatorial problems,is applicable for such contingency screening problems. In the GA-based contingency screening method, a double line outage is represented as a chromosome. The fitness of the chromosome for environment is defined by using the transient energy function of power systems. The new procedure for avoiding the loss of important outage during contingency screening is developed and embedded in the proposed method by using the theorem of schema for GA. The validity of the proposed method is confirmed by applying it to a 6-machine 30-bus 40-line system. The result shows that the GA-based contingency screening has the potential for practical use
Abstract: This paper proposes an application of genetic algorithm (GA) to contingency screening in power systems. The contingency selected by the GA-based screening method is the double line outage which has the risk of causing transient instability. Generally, the contingency screening problem including multiple outage can be interpreted as the combinatorial optimization one for searching the combination of single outages which makes the system insecure. Therefore, GA which is one of the probabilistic searches for combinatorial problems,is applicable for such contingency screening problems. In the GA-based contingency screening method, a double line outage is represented as a chromosome. The fitness of the chromosome for environment is defined by using the transient energy function of power systems. The new procedure for avoiding the loss of important outage during contingency screening is developed and embedded in the proposed method by using the theorem of schema for GA. The validity of the proposed method is confirmed by applying it to a 6-machine 30-bus 40-line system. The result shows that the GA-based contingency screening has the potential for practical use
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