Comparison of hybrid differential evolution algorithm with genetic algorithm based power system security analysis using FACTS

Abstract

This paper presents a novel stochastic hybrid differential evolutionary algorithm technique to find the optimal location of Flexible Alternating Current Transmission System (FACTS) devices with minimum cost of installation and to improve power system security and is compared with Genetic Algorithm (GA). Differential Evolution (DE) algorithm technique is a simple evolutionary search algorithm and shows better performance but greedy in space searching. Particle Swarm Optimization (PSO) converges quickly and but stuck in local optima. A novel heuristic method based on Genetic algorithm also used to find optimal location of FACTS devices to enhance the power system security and no absolute assurance of global optimum. In this paper hybrid differential evolutionary algorithm (DEPSO) is introduced to eliminate the problems of DE and PSO and solve the power system security problem with greater accuracy and compared with Genetic Algorithm. The proposed algorithm minimizes the security index, loss and the installation cost of FACTS devices in the transmission network. Security index indicates the overload level of the transmission lines. Three types of FACTS devices, Static Var Compensator (SVC), Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC) are considered and the proposed algorithm is verified by standard IEEE 14 bus network.