Robust soft computing control algorithm for sustainable enhancement of renewable energy sources based microgrid: A hybrid Garra rufa fish optimization – Isolation forest approach

Abstract

A novel hybrid Garra rufa Fish optimization (GRFO) – isolation Forest (iForest) soft computing approach is proposed in this paper for optimizing the controller parameters in an isolated test microgrid. The test microgrid comprises of two PV units and one wind generator synchronized via Voltage Source Converters (VSC) in the ac side. Each VSC is regulated based on power generation in individual micro sources. The operation of VSC is the key factor maintaining the stability of the micro grid. Each VSC is controlled by outer power loop and inner current regulation loop employing PI controllers. The performance of the VSC is enhanced using the proposed hybrid GRFO – iForest algorithm for tuning the PI controller gains. The performance of the proposed approach is then compared with conventional tuning PI controller and optimization techniques like Ant Lion Optimization (ALO) and Modified Ant Lion optimization based Artificial Neural Network (MALANN). The gain parameter of the proposed controller is optimally tuned and the controller provides reliable sustainable microgrid system operation. The proposed approach is simulated in MATLAB/Simulink and validated using OP-4500 Real Time Simulator environment. Based on the results, the performance of the GRFO-iForest approach is superior during steady-state and transient operation. It enhances the sustainability of the microgrid by restoring normal operating conditions after a small physical disturbance and the microgrid remains stable with the optimized regulator. The stability of the overall system is proved with the mathematical model.