A Levy Flight motivated meta-heuristic approach for enhancing maximum loadability limit in practical power system

Abstract

Most of the practical engineering optimization problems are highly nonlinear, nonconvex, and sometimes discontinuous. Classical optimization techniques, mostly being differential calculus based, either fail to find the optimal solution for practical problems or provide solution after relaxing the nonlinearities. Over the time, population-based meta-heuristic techniques have gained enough popularity among research fraternity due to their unrestricted performance on the nature of the optimization problems. Despite their better performances, they sometimes suffer from the problem of trapping into local optima. Hence, suitable strategies should be adopted to overcome the above said issues. In view of this, a new Levy Flight (LF) based Adaptive Particle Swarm Optimization (APSOLF) technique is designed and proposed in this work to solve complex, nonlinear optimization problems. The performance of the proposed algorithm is gauged by applying it on various mathematical benchmark functions. The technique is also applied to solve the practical electrical engineering problems where the task of proposed algorithm is to optimize the Static Synchronous Series Compensator (SSSC) parameters to improve the Maximum Loadability Limit (MLL) of some standard test power systems viz. IEEE 14, 30, 57, 118 and a practical Indian southern region 205 buses. The results obtained are compared with other variants of PSO. Furthermore, the robustness of the proposed algorithm is tested by performing the statistical analysis (both parametric and nonparametric tests). The results confirm better efficiency, robustness, and consistency of the proposed algorithm. The simulations are performed on MATLAB environment.