Hybrid Algorithm Approach for Driving Distribution Static Compensator in Multiple Renewable Energy Systems

Abstract

There has been a recent drive toward integration of renewable energy resources into the main grid to improve the environmental aspects of the energy generation sector. The integration brings about stability and quality issues that need to be handled in order to achieve smooth operation at the load end. This involves issues like voltage sags, swells, and harmonic distortions which are the key focused areas in our research. The distribution static compensators (DSTATCOMs) are being used in this article for a four-machine system integrated with multiple renewable energy resources like solar, wind, and fuel cells in order to enhance power quality issues at the loading end. The integration of sophisticated control mechanisms, such as neural networks (NNs) and metaheuristic algorithms like the bat optimization algorithm (NN_BAT), was analyzed for the performance of system dynamics under disturbances. The voltage sag and swell conditions were created in different phases of a three-phase line. It was found that adaptive and predictive adjustments to the electrical network's dynamic conditions ensured a total harmonic distortion (THD) percentage of 1.22% and 1.72% in line voltage and current, respectively, when voltage swell occurred. Also, when the system was subjected to sag in voltage, the THD% was 1.29% and 1.86% for voltage and current, respectively. It was found that hybrid strategy NN_BAT consistently performs better, achieving lower THD percentages in both voltage and current which indicates a more effective control in mitigating distortions and maintaining power quality.