A new hybrid GA-GSA algorithm for tuning damping controller parameters for a unified power flow controller

摘要

Tuning of damping controller parameters for optimal setting, such as gain and signal wash out block parameters, has major effects on its performance improvement. Estimation of optimum values for these parameters requires reliable and effective training methods so that the error during training reaches its minimum. This paper presents, a suitable tuning method for optimizing the damping controller parameters using a novel hybrid Genetic Algorithm-Gravitational Search Algorithm (hGA-GSA). The primary purpose is that the FACTS based damping controller parameter can be optimized using the proposed method. The central research objective here is that, how the system stability can be improved by the optimal settings of the variables of a damping controller obtained using the above proposed algorithm. Extensive experimental results on different benchmarks show that the hybrid algorithm performs better than standard gravitational search algorithm (GSA) and genetic algorithm (GA). In this proposed work, the comparison of the hGA-GSA algorithm with the GSA and GA algorithm in term of convergence rate and the computation time is carried out. The simulation results represent that the controller design using the proposed hGA-GSA provides better solutions as compared to other conventional methods. (C) 2015 Elsevier Ltd. All rights reserved.