Optimal Performance of Self Excited Induction Generator Using Teaching Learning-Based Optimization Algorithm and Static Var Compensator

Abstract

The paper presents an application of Teaching Learning-Based Optimization (TLBO) algorithm to improve the performance of self-excited induction generators (SEIG). Two control methods of SEIG have been studied. The first method, the TLBO algorithm is applied to generate the optimal capacitance to maintain rated voltage with constant prime mover speed. The drawback of this method is the generator frequency decreases with load and to overcome this disadvantage, the other control method is proposed. In the proposed method, the TLBO is used to obtain optimal capacitance and prime mover speed to have rated load voltage and frequency. The Static VAR Compensator (SVC) of fixed capacitor and controlled reactor is used to control the reactive power. The parameters of SVC are obtained by using TLBO algorithm. The performance of the SEIG at different loads and prime mover speeds using TLBO algorithm is realized. A whole system of three phase induction generator and SVC is established under MatLab/Simulink environment. The performance of the SEIG is demonstrated on two different ratings (i.e. 10 hp and 2hp). An experimental setup is built-up using a 2 hp induction motor to confirm the theoretical analysis. Good agreement between results confirms and signifies the viability of the proposed TLBO-based methodology.