Squirrel cage induction generator based micro grid voltage assessment with STATCOM using different metaheuristic approaches

Squirrel Cage Induction Generators are the most common machine to operate wind-based power plants. Microgrids are small-scale electrical systems that provide power to a local area, and optimization methods for microgrids aim to ensure that these systems operate in a way that is both efficient and cost-effective. In such applications, STATCOM is used as reactive power provider and It is necessary to use advanced controllers like the Genetic Algorithm (GA), Firefly Algorithm (FA), and Adaptive Neuro Fuzzy Inference System (ANFIS) as the usual approaches for setting gain factors of STATCOM controller which does not perform well in the case of sudden voltage level changes due to disturbances. The firefly algorithm is primarily required to estimate the gain factors of the STATCOM controller which is used in hybrid micro grid with SG and SCIG for controlling the output voltage in the occurrence of large probabilistic ambiguity. The effectiveness of firefly controlled STATCOM controller for voltage-reactive power control in hybrid microgrid is further examined and validated by comparing the results with other GA, ANN and ANFIS tuning techniques. The need for reactive power is accomplished by STATCOM to minimize the time required to control voltage transient response. The main contribution of this paper includes: an assessment of STATCOM performance with step variations in the input wind energy and reactive power load requirement under conditions of severe probabilistic uncertainty, system analyses conducted under dynamic conditions rather than using a composite load model. Additionally, a comparison of reactive power and voltage control by STATCOM is presented, and employing FA techniques to improve, a comparative of STATCOM reactive power and voltage control. Results comparing all tuning techniques shows that utilizing the Integral of Square of Errors (ISE) criterion, sophisticated tuning techniques can maintain excellent performances under a variety of disruptions.