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In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm namely Gravitational Search Algorithm (GSA) based on the law of gravity and mass interactions is illustrated for designing Static Synchronous Series Compensator (SSSC) for single and multimachine power systems. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in tuning SSSC compared with Bacteria Foraging (BF) and Genetic Algorithm (GA). Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power systems stability over a wide range of loading conditions. [ABSTRACT FROM AUTHOR]

A new metaheuristic method, the Cuckoo Search (CS) algorithm, based on the life of a bird family is proposed in this paper for optimal design of static synchronous compensator (STATCOM) in a multimachine environment. PV curves are illustrated to determine the best location of STATCOM. The STATCOM parameter tuning problem is converted to an optimization problem which is solved by CS Algorithm. The performance of the proposed CS based STATCOM (CSSTATCOM) is compared with Genetic Algorithm (GA) based STATCOM (GASTATCOM) and open loop STATCOM under various operating conditions and disturbances. The superiority of the proposed technique in damping oscillations and enhancing voltage profiles is confirmed through eigenvalues and time domain simulation results over the GA and open loop one. [ABSTRACT FROM AUTHOR]

A new optimization technique, Imperialist Competitive Algorithm (ICA), which is inspired from socio-political phenomenon of imperialistic competition, is proposed in this paper for optimal design of static synchronous compensator (STATCOM) in a multimachine environment. PV curves are illustrated to deduce the best location of STATCOM. The STATCOM parameter tuning problem is converted to an optimization problem which is solved by ICA. The performance of the proposed ICA based STATCOM (ICASTATCOM) is compared with Genetic Algorithm (GA) based STATCOM (GASTATCOM) and open loop STATCOM under various operating conditions and disturbances. The superiority of the proposed technique in damping oscillations and enhancing voltage profiles over a wide range of operating conditions and system configurations is confirmed through eigenvalues and time domain simulation results. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes Bacteria Foraging Optimization Algorithm (BFOA) based Static Var Compensator (SVC) for the suppression of oscillations in a multimachine power system. The proposed design problem of SVC over a wide range of loading conditions is formulated as an optimization problem. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed technique has been evaluated with the performance of Genetic Algorithm (GA) to demonstrate the superior efficiency of the proposed BFOA in tuning SVC controller. Simultaneous tuning of the Bacteria Foraging based SVC (BFSVC) gives robust damping performance over wide range of operating conditions in compare to optimized SVC controller based on GA (GASVC). [Copyright &y& Elsevier]

Abstract: This paper develops a novel algorithm for simultaneous coordinated designing of power system stabilizers (PSSs) and static var compensator (SVC) in a multimachine power system. The coordinated design problem of PSS and SVC over a wide range of loading conditions is formulated as an optimization problem. The Bacterial Foraging Optimization Algorithm (BFOA) is employed to search for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the bacterial foraging based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BFOA (BFPSS) and optimized SVC controller based on BFOA (BFSVC). Moreover, a statistical T test is performed to ensure the effectiveness of coordinated controller versus uncoordinated one. [Copyright &y& Elsevier]