Chennai and Dr.MGR University Second International Conference on Sustainable Energy and Intelligent System (SEISCON 2011) , Dr. M.G.R. University, Maduravoyal, Chennai, Tamil Nadu, India. July. 20-22, 2011. 346 Comparison of PSO Models for Optimal Placement and Sizing of Statcom Sarika Varshney, Laxmi Srivastava, Manjaree Pandit Madhav Institute of Technology, Gwalior, India Keywords: Flexible AC Transmission Systems, Multi-Objective Particle Swarm Optimization, PSO-Time varying Acceleration Constants, Static synchronous compensator. Abstract Voltage profile has been a major concern for power system utili- ties because of several events of voltage collapse in the recent past. Flexible AC Transmission Systems (FACTS) devices are increas- ingly used to improve voltage profile and power flow control in many utilities. However, owing to the considerable cost of FACTS devices involved, it is important to find the optimal location and siz- ing of these devices in a power system to obtain maximum benefits of these devices. For finding optimal location and sizing of Statcom in a power system, this paper investigates the application of vari- ous models of Particle Swarm Optimization (PSO) algorithms. To compare the performance of various PSO algorithms, these were implemented for optimal location and sizing of Statcom for voltage profile improvement in a benchmark 5-bus system. Out of various PSO models: classical PSO, PSO time varying inertia weight (PSO- TVIW), PSO random inertia weight (PSO-RANDIW) and PSO- Time varying acceleration coefficients (PSO-TVAC), PSO-TVAC model is found to be superior in terms of computation speed and quality of solution. 1 Introduction In the last few years, voltage collapse problems in power systems have been of paramount concern for electric utilities, as several major blackouts throughout the world have been directly asso- ciated to this phenomenon[2]. To avoid the severe and undesir- able event of voltage collapse, this is essential to maintain voltage profile of a power system. To improve the voltage profile of the power system an alternative solution is to locate an appropriate Flexible AC transmission system (FACTS) device like SVC, Statcom etc. FACTS devices are the solid state converters having capability of improving power transmission capacity, improving voltage profile, enhancing power system stability and security, minimizing transmission losses etc. In order to optimize and to obtain the maximum benefits from their use, the main issues to be considered are the type of FACTS devices, the settings of FACTS devices and optimal location of FACTS devices[17,19]. FACTS devices include static var compensator (SVC), static syn- chronous compensator (Statcom), thyristor controlled series com- pensator (TCSC), unified power flow controller (UPFC) etc. TCSC is connected in series with the transmission line to compensate for inductive reactance of the transmission line. SVC and Statcom are connected in shunt with the system. Though, the primary purpose of Statcom is to support bus voltage by injecting or absorbing the reactive power by means of thyristor controlled elements, it is ca- pable of improving the power system stability also. The UPFC is capable of providing voltage, active and reactive power control and it regulates all the three variables simultaneously [9,18]. Owing to the huge cost of FACTS devices involved, it is impor- tant to find the optimal location and sizing of these devices in a power system to obtain maximum benefits of these devices. The conventional optimization methods such as mixed integer linear and non linear programming have been intensively investigated to resolve this major issue; however these are time consuming, as they are iterative, and require heavy computational burden and slow convergence. In addition, the search process is susceptible to be trapped in local minima and the solution obtained may not be optimal[8,14]. Recently, nature inspired computation techniques such as Genetic Algorithms (GA)[3,6,7], Evolutionary programming[13,17,19] and Particle Swarm Optimization(PSO) have been employed to obtain the optimal location of FACTS devices with promising results. The evolutionary techniques constitute an approach to search for the optimal solution via some form of directed random search process. A relevant characteristic of evolutionary meth- ods is that they search for solutions without previous problem knowledge. PSO is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling[10,11]. In ref.[19], a non-dominated sorting PSO has been proposed for optimal location of SVC and TCSC, while in ref.[18], PSO based technique has been implemented for optimal location of various FATCS devices considering the cost of instal- lation and system loading. Voltage security has been considered for optimal location of SVC using Multi-objective PSO (MOPSO) in[12]. Classical PSO based technique has been implemented for optimal location of Statcom considering voltage deviation[8] and transient stability[14]. PSO based techniques have become popular, as these are sim-