Standalone SEIG using Smart Meta-Heuristic Algorithm to Optimize its Performance and Loadability

Induction generator used in offshore areas for wind conversion has the disadvantage of poor terminal voltage. This is owing to the unpredictability of wind speed. Therefore, this paper primarily emphasis on performance of induction generator under self-excitation. In an effort to improve its terminal voltage, the optimal values of capacitance have been chosen in this paper for different configurations. In doing so, three meta-heuristic algorithms named as Plant Propagation Algorithm based on strawberry plant colonization, Genetic Algorithm based on natural selection and Gravitational Search Algorithm based on Law of gravity have been used and compared. For the optimized outputs, significant constraints such as equality, inequality, and bounded limitations are taken into account. This study focused on both resistive and inductive types of loads. The findings of the simulation results shows that the plant propagation algorithm regulates voltage and frequency better than the gravitational search algorithm and genetic algorithm in self-excited induction generator machines.