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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]

Cuckoo Search (CS) algorithm is introduced in this paper for optimal Power System Stabilizers (PSSs) design in a multimachine power system. The PSSs parameter tuning problem is formulated as an optimization problem which is solved by CS Algorithm. An eigenvalues based objective function involving the damping ratio, and the damping factor of the lightly damped electromechanical modes is considered for the PSSs design problem. The performance of the proposed CS based PSSs (CSPSS) has been compared with Genetic Algorithm (GA) based PSSs (GAPSS) and the Conventional PSSs (CPSS) under different operating conditions and disturbances. The results of the developed CSPSS are verified through time domain analysis, eigenvalues and performance indices. Also, the effectiveness of the proposed algorithm in providing good damping characteristics is confirmed. [ABSTRACT FROM AUTHOR]

Highlights: [•] BFOA is proposed to tune the parameters of PID controller for nonlinear LFC problem. [•] A two area power system is considered to demonstrate the proposed method. [•] BFOA is employed to search for optimal controller parameters. [•] The effectiveness of the proposed controller in damping oscillations is validated. [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 proposes Bacteria Foraging Optimization Algorithm (BFOA) based Thyristor Controlled Series Capacitor (BFTCSC) for the suppression of oscillations in a multimachine power system. The proposed design problem of TCSC over a wide range of loading conditions is formulated as an optimization problem with an eigenvalue based objective function. BFOA is employed to search for optimal controller parameters. 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 TCSC controller. Simultaneous tuning of the BFTCSC gives robust damping performance over a wide range of operating conditions in compare to optimized TCSC controller based on GA (GATCSC). [Copyright &y& Elsevier]

Abstract: Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations. [Copyright &y& Elsevier]

This paper proposes a speed control of Switched Reluctance Motor (SRM) supplied by Photovoltaic (PV) system. The proposed design of the speed controller is formulated as an optimization problem. Ant Colony Optimization (ACO) algorithm is employed to search for the optimal Proportional Integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of Genetic Algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature, and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions. [ABSTRACT FROM AUTHOR]