Natural Genetics Adapted Control for an Autonomous Wind-Battery Based Microgrid

Autonomous microgrids supply power to large remote areas where access to the grid is infeasible. The generation of these microgrids is highly dominated by renewable energy resources (RESs) equipped with a storage battery. Due to the uncertainty associated with RESs, the sustainability and reliability of supply become the prime areas of focus. The battery reduces the uncertainty during demand rise, power deficiency, and overloading with their faster response than diesel generators and microturbines. In this article, the power generation of the isolated microgrid is considered from the wind energy source along with a battery. For the favorable speed regulation of the wind turbine driven generator, the traditional proportional integral (PI) controller is replaced by natural genetics [genetic algorithm (GA)] and evolution inspired PI controller. The issues identified by the use of a fixed gain PI controller such as unsuitable regulation due to incorrect tuning and discontinuous tuning are addressed by the use of a GA tuned speed controller. The performance of the PI controller degrades while dealing with the ac quantities, so in terms of dealing with sinusoidal signals, here an adaptive proportional resonant controller with a combined generalized integrator based phase locked loop is utilized. It tracks the reference ac voltage and provides frequency adaptation in the presence of harmonics pollution with zero steady state error. The batteries deployed through a bidirectional converter, act as the residential storage for backing up the power firming renewable production. The maximum power point (MPP) extraction of the wind generator is carried by a perturb and observe based MPP technique. The performance improvement obtained from the laboratory developed test setup verifies the microgrid effectiveness under renewable generation variability and load alterations.