Comparative Analyses amongst 3 Hybrid Controllers - MPC-HGAFSA, LQR-HGAFSA and PID-HGAFSA in a Micro Grid Power System Using MAD and RMSE as Measures of Performance Metrics

Abstract

Communication in Physical Sciences, 2023, 10(1): 155-162

Authors: Godwin Ezikanyi Okey, Yusuf Jibril and Gbenga Abidemi Olarinoye

Received:  21 June 2023/Accepted 09 November 2023

Adverse effects in a power generating system, such as frequency instability, voltage profile degradation, poor power delivery and power outages, are caused by frequency and voltage fluctuations. These fluctuations are caused by load variations and generation losses, which are inevitable in a power-generating system. Power controllers such as model predictive controller (M.P.C.), linear quadratic regulator (L.Q.R.) and proportional integral derivative (P.I.D.); and controller optimizers such as genetic algorithm (G.A.), artificial fish swarm algorithm (AFSA)  and particle swarm optimization (PSO) with their hybrids are often used to mitigate the aforementioned effect. This paper tends to compare the efficiency of each of the three mentioned controllers with the optimizers using mean absolute deviation (MAD) and root mean square error (RMSE) as performance metrics. The hybrid of .A.G.A. and AFSA (HGAFSA) was used to optimize each of the controllers (MPC-HGAFSA, LQR-HGAFSA and PID-HGAFSA) in a micro-grid power system. the M.P.C.- HGAFSA based approach demonstrates an outstanding frequency and voltage control capability when compared with the other two control strategies, while PID-HGAFSA-based strategy is the least performing strategy.