Performance Analysis of Advanced Metaheuristics for Optimal Design of Multi-Objective Model Predictive Control of Doubly Fed Induction Generator

Finite control set model predictive control (FCS-MPC) is an attractive control method for electric drives. This is primarily due to the ease of implementation and robust responses. When applied to rotor current control of the Doubly Fed Induction Generator (DFIG), FCS-MPC has thus far exhibited promising results when compared to the conventional Proportional Integral control strategy. Recently, there has been research conducted regarding the reduction in switching frequency of FCS-MPC. Preliminary studies indicate that a reduction in switching frequency will result in larger current ripples and a greater total harmonic distortion (THD). However, research in this area is limited. The aim of this study is two-fold. Firstly, an indication into the effect of weighting factor magnitude on current ripple is provided. Thereafter, the research work provides insight into the effect of such weighting factor on the overall current ripple of FCS-MPC applied to the DFIG and attempts to determine an optimal weighting factor which will simultaneously reduce the switching frequency and keep the current ripple within acceptable limits. To tune the relevant weighting factor, the utilization of swam intelligence is deployed. Three swarm intelligence techniques, particle swarm optimization, the African Vulture Optimization Algorithm, and the Gorilla Troops Optimizer (GTO), are applied to achieve the optimal weighting factor. When applied to a 2 MW DFIG, the results indicated that owing to their strong exploitation capability, these algorithms were able to successfully reduce the switching frequency. The GTO exhibited the overall best results, boasting steady-state errors of 0.03% and 0.02% for the rotor direct and quadrature currents whilst reducing the switching frequency by up to 0.7%. However, as expected, there was a minor increase in the current ripple. A robustness test indicated that the use of metaheuristics still produces superior results in the face of changing operating conditions. The results instill confidence in FCS-MPC as the control strategy of choice, as wind energy conversion systems continue to penetrate the energy sector.