Determination of capacitance range in the self-excited induction generator through the hybrid genetic algorithms

The self-excited induction generators (SEIGs) are mainly demanded for generation of electricity in remote areas due to several advantages such as less maintenance, rugged construction etc. The SEIG's excitation is usually supplied from a capacitor bank whose capacitance plays major role in transferring power into the load. Previous investigation has shown that excitation is only possible between minimum and maximum capacitances and the resonant capacitance lies down in this range. Determination of these capacitances involves in a numerical solution of transcendental equations extracted from the steady state equivalent circuit of the SEIG. Although the Newton-Raphson method is extensively used to solve these equations however it is inefficient in most cases. In this paper, the hybrid genetic algorithms (HGA) technique is proposed to solve these equations to obtain necessary capacitance range for excitation under ohmic, inductive and capacitive load conditions. The proposed approach produces meaningful and encouraging outcomes for understanding the SEIG's performance from various aspects.