Optimization of a static VAR compensation parameters using PBIL

Abstract

Static VAR Compensators (SVCs) are part of the family of the Flexible Alternating Current Transmission Systems (FACTS) devices that are predominantly used for quick and reliable line voltage control. Under contingency conditions, they can be used to provide dynamic, fast response reactive power. In addition, SVCs can be used to increase power transfer capability, and damp power oscillations. This paper is concerned with damping of power oscillations using SVC. To perform the function of damping controller, supplementary control is required for the SVC. In addition, the control parameters for the SVC need to be tuned adequately. In this paper, Population Based Incremental Learning (PBIL) is used to tune the parameters of SVC in a multi machine power system. Population-Based Incremental Learning (PBIL) is a technique that combines Genetic Algorithm and simple competitive learning derived from Artificial Neural Network. It has recently received increasing attention due to its effectiveness, easy implementation and robustness. To show the effectiveness of the approach, simulations results are compared with the results obtained using simple Genetic Algorithm (SGA) and the conventional SVC under different operating conditions.