Optimal Location of Facts Devices Using Adaptive Particle Swarm Optimization Mixed with Simulated Annealing

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO), called Adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA) that will be named APSO-SA is introduced. This algorithm uses a novel PSO algorithm (APSO) to increase convergence rate and incorporate the ability of SA to avoid being trapped in local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, Thyristor Controlled Series Capacitor (TCSC) and Static VAR Compensator (SVC) are considered. The main objectives of presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases namely, single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC) are considered. Using the proposed method, the locations, type and sizes of FACTS devices are obtained for reaching the optimal objective function. APSO-SA is used to solve the above non–linear programming problem for better accuracy and fast convergence. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the standard PSO algorithm. The optimization results are compared with standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14-bus systems by MATLAB soft ware. Numerical results demonstrate that the APSO-SA is fast and has much less computational cost.