Application of Adaptive Tabu Search Algorithm in Sinusoidal Fryze Voltage Control based Hybrid Series Active Power Filter

Abstract

A novel hybrid series active power filter to eliminate harmonics and compensate reactive power is presented and analyzed. The proposed active compensation technique is based in a hybrid series active filter using adaptive Tabu search (ATS) algorithm in the conventional Sinusoidal Fryze voltage (SFV) control technique. Optimization of the conventional Sinusoidal Fryze voltage control technique has been done using adaptive tabu search algorithm. This paper discusses about the comparative performances of conventional Sinusoidal Fryze voltage control strategy and ATS-optimized controllers. ATS algorithm has been used to obtain the optimum value of Kp and Ki. Analysis of the hybrid series active power filter system under non linear load condition and its impact on the performance of the controllers is evaluated. Effectiveness of the hybrid series active power filter to provide harmonic damping is demonstrated by MATLAB/Simulink results. Total harmonic distortion (THD) demonstrates the practical viability of the controller for hybrid series active power filter to provide harmonic isolation of non-linear loads and to comply with IEEE 519 recommended harmonic standards.

Optimal Allocation of Static Synchronous Series Compensator Controllers using Chemical Reaction Optimization for Reactive Power Dispatch

“…Therefore, these approaches are not appropriate for solving ORPD. To overcome these limitations, the robust and flexible evolutionary optimization techniques such as, simple genetic algorithms " Iba (1994)", evolutionary strategies " Bhagwan, and Patvardhan (2003)", evolutionary programming "Liang, Chung, Wong and Duan(2006) ", particle swarm optimization " Yoshida, Fukuyama, Kawata, Takayama and Nakanishi (2000)", differential evolution " Liang, Chung, Wong, Duzn, and Tse (2007)", real coded genetic algorithms (RGA) " Subbaraj and Rajnaryanan(2009)", tabu search (TS) " Khalid, Kumar, Mishra & (2014)" simulated annealing (SA) " Dao, Zelinka, & Duy, H. (2012)", teaching learning based optimization (TLBO) " Mukherjee, Paul, & Roy, (2015)", cultural algorithm (CA) "Som., & Chakraborty, (2012)", improved particle swarm optimization (IPSO) "Polprasert, Ongsakul, & Dieu, (2013)", biogeography based optimization (BBO) " Kamboj, & Bath, (2014)" and firefly algorithm (FA) " have been applied. These evolutionary algorithms have shown success in solving the ORPD problems since they do not need the objective and constraints as differentiable and continuous functions.…”