Series compensation assessment of self-excited induction generator using genetic algorithm

Abstract

As the self-excited induction generator (SEIG) is the most suitable for remote and windy areas, so the pre-evaluation of this generator performance is essential. For better voltage profile and power capability, series compensation can be used. This paper presents an assessment of the shunt, short-shunt and long-shunt compensation configurations. A generalized mathematical model of SEIG is presented. The effectiveness of the generalized mathematical model is then tested on a three-phase, 1.5-kW induction generator operating in all configurations. Proposed Genetic Algorithm (GA) model has been implemented to predict the output power capability and corresponding load voltage for each series compensation under various loading conditions. Results obtained from the proposed (GA) model have been compared with the experimental and simulation results. The comparison confirms the validity and accuracy of the proposed GA model as an assessment tool of induction generator with different compensation configurations.

Keywords:

• Self-excited induction generator
• genetic algorithm
• series compensation