Simultaneous stabilization of multimachine power systems via genetic algorithms

Abdel-Magid, Y.L.; Abido, M. A.; Al-Baiyat, S.A.; Mantawy, A.H.

doi:10.1109/59.801907

Cited by 214 publications

(93 citation statements)

References 10 publications

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“…The initial values of premise parameters are set in such a way that the MF's are equally spaced in the range [-1 1]. The outputs of the inference system are linear membership functions and the rule base with five fuzzy if-then rules of (TS) Takagi and Sugeno's type given by if Δω is A1 and Δω(t-1) is A2 then fi = pi Δω + qi Δω(t-1) + ri (15) Where Δω and Δω(t-1) are the inputs of the systems while A1 and A2 are fuzzy sets in the antecedent, and pi, qi and ri are the consequent parameters.…”

Section: Structure Of Granfis-pssmentioning

confidence: 99%

Designing Power System Stabilizer for System Damping for Transient Disturbances Using Grey ANFIS Technique

Srivastav¹,

Jha²,

Qureshi³

2014

IJIRSET

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This paper describes a design procedure for a Grey ANFIS based power system stabilizer (GrANFIS-PSS) and investigates their robustness for a multi-machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the GrANFIS-PSS. A four-machine and a two-area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead-lag based power system stabilizer controller. The simulation result shows that the GrANFIS-PSS can be designed to achieve good performance merely using the combination of Grey prediction and Adaptive Neuro-Fuzzy Inference System (ANFIS). GrANFIS-PSS is designed to damp out the low frequency local and inter-area oscillations of the Multi-machine power system. By applying this GrANFIS-PSS to the power system the damping of inter-area modes of oscillations in a multi-machine power system is handled properly. The effectiveness of the proposed GrANFIS-PSS is demonstrated on two area four machine power system (Kundur system), which has provided a comprehensive evaluation of the learning control performance. Finally, several fault and load disturbance simulation results are presented to stress the effectiveness of the proposed GrANFIS-PSS in a multimachine power system and show that the proposed intelligent controls improve the dynamic performance of the GrANFIS-PSS and the associated power network

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Section: Structure Of Granfis-pssmentioning

confidence: 99%

Designing Power System Stabilizer for System Damping for Transient Disturbances Using Grey ANFIS Technique

Srivastav¹,

Jha²,

Qureshi³

2014

IJIRSET

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“…In [185], a robust controller design technique based on genetic algorithm is used to simultaneously tune PSS for multiple operating conditions. In [186], a genetic algorithm is used for the simultaneous stabilization of multi-machine power systems over a wide range of operating conditions via single setting power system stabilizers. Sebaa and Boudour et al [209] has been suggested a genetic algorithm for coordinated design of PSSs and SVC-based controllers in power system to enhance power system dynamic stability.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…The various optimization based methods have been proposed in literatures for coordination includes a dynamic optimization programming algorithms [128], nonlinear optimization programming techniques [129]- [133], [184], linear optimization programming techniques [134]- [136], [183], [203]- [204], [206]- [207], [210], immune-based optimization algorithms [137]. The various artificial intelligence (AI) based methods have been proposed in literatures that includes a genetic algorithms (GA) [144]- [147], [185], [186], [209], tabu search algorithms [148], [187], simulated annealing (SA) based approach [149], particle swarm optimization (PSO) techniques [150], [190], [205], artificial neural networks (ANN) based algorithms [ [151], [202],fuzzy logic based approach [152]- [155], [188]- [189], [212], adaptive neuro-fuzzy inference system (ANIS) techniques [156], H-infinity optimization techniques [191]- [193], μ -synthesis techniques [194]- [196], linear matrix inequality technique [197], prony methods [198], riccati equations methods [199], relative gain array (RGA) theory [208], load flow control technique …”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Optimal Placement and Coordinated Control Techniques of FACTS Controllers in Multi-Machine Power System Environments

Singh¹,

Sharma²,

Tiwari³

2010

Journal of Electrical Engineering and Technology

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-This paper presents exhaustive review of various methods/techniques for coordinated control between FACTS controllers in multi-machine power systems. It also reviews various techniques/methods for optimal choice and allocation of FACTS controllers. Authors strongly believe that this survey article will be very much useful to the researchers for finding out the relevant references in the field of placement and coordination of FACTS Controllers.

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“…The entire population is replaced by offsprings using crossover and mutation in standard genetic algorithm [9], whereas in proposed Genetic Algorithm the parents are selected on the basis of fitness value and the best parent chromosome is retained, Comparing the fitness values of both the parents and the children, the best stings will go for the next generations. The GA stops when a predefined maximum number of generations is achieved or when the value returned by the objective function, being below a threshold, remains constant for a number of iterations.…”

Section: Design Constraintsmentioning

confidence: 99%

“…The advantage of the GA technique is that it is independent of the complexity of the performance index considered. The application of GAs to tune the parameters of PSS have been reported [13]- [15]. In view of the above, the main thrust of the research work presented in this paper is to design power system stabilizers, which simultaneously stabilize the multimachine power system.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Stabilization of Multimachine Power System Using Genetic Algorithm Based Power System Stabilizers

Dubey

2006

Proceedings of the 41st International Universities Power Engineering Conference

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-This paper presents the simultaneous tuning of power system stabilizers in a multi-machine power system. The problem of selecting the parameters of power system stabilizers in converted into an optimization problem that is solved by genetic algorithm using eigen value based objective function. The dynamic performance of the system has been investigated under small perturbation and large disturbance. The performance of genetic algorithm based PSS has been compared with the conventional power system stabilizer. The non-linear simulation results on 4 machine, 11 bus system verifies the effectiveness of the proposed genetic algorithm based power system stabilizers.

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Simultaneous stabilization of multimachine power systems via genetic algorithms

Cited by 214 publications

References 10 publications

Designing Power System Stabilizer for System Damping for Transient Disturbances Using Grey ANFIS Technique

Designing Power System Stabilizer for System Damping for Transient Disturbances Using Grey ANFIS Technique

A Comprehensive Survey of Optimal Placement and Coordinated Control Techniques of FACTS Controllers in Multi-Machine Power System Environments

Simultaneous Stabilization of Multimachine Power System Using Genetic Algorithm Based Power System Stabilizers

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