SVC Parameters Optimization Using a Novel Integrated MCDM Approach

Abstract

Nowadays, multi-criteria decision-making (MCDM) methods are used widely in many fields of research and applications. Many studies have shown that MCDM approaches are effective in determining the optimal solution to a variety of symmetrical and asymmetrical problems with numerous parameters. This article investigates a novel approach using multi criteria decision making (MCDM) to optimize the parameters of static var compensator (SVC) and power system stabilizers (PSS). The proposed technique integrates similarity membership function reduction algorithm (SMFRA), removal effects of criteria (REC) and combined compromise solution (CoCoSo). In the first stage, (SMFRA) is employed to select the most dominant controller parameters in the optimization process. Secondly, the weights of the reduced parameters are computed based on (REC). Finally, (CoCoSo) method searches for the optimal setting parameters. A detailed sensitivity analysis is presented to evaluate the obtained results. It is found that the suggested integrated technique is time saving, easily implemented and of low computation burden, which can successfully be implemented to solve a wide range of issues, both comparable and dissimilar.