Selection of Capacitors for Compensated Self-Excited Induction Generator using Meta-Heuristic Approach

Abstract

Renewable and sustainable energy is increasingly gaining interest in current research circles. Self excited induction generators (SEIG) are being deployed for the exploitation of power from renewable sources for a long time and they are becoming more and more popular for supplying electrical energy to remote and rural areas for its various advantages over synchronous generators. The SEIG has poor voltage regulation under changing loads and this is one of the major drawbacks due to which the wide applicability of the generator is restricted. Hence the steady state analysis of the machine becomes of paramount importance. Many conventional methods have been used to predict the generator performance under various loading conditions. Followed by the steady state analysis, many conventional methods have been developed for the system consisting of series and shunt capacitors to achieve better voltage regulation at various loading conditions. The values of the capacitors are selected so as to minimize the number of switched capacitors needed to satisfy the voltage regulation criteria. The selection strategy of the series and shunt capacitors for desired voltage regulation is made through a conventional MATLAB routine ‘fsolve’ where in the generator characteristics are evaluated for resistive and resistive-inductive loads. In the present thesis work, the results achieved by the conventional method are compared with a meta- heuristic search technique called particle swarm optimization (PSO) to get the optimum set of series and shunt capacitors under different loading conditions. The performances using both the techniques are compared and the effectiveness of PSO is seen as the voltage regulation is slightly improved.