Fuzzy grey wolf optimization for controlled low-voltage ride-through conditions in grid-connected wind turbine with doubly fed induction generator

Abstract

With its enormous environmental and monetary benefits, the wind turbine has become an acceptable alternative to the generation of electricity by fossil fuel or nuclear power plants. Research remains focused on improving the performance of wind turbines with maximum flexibility and gains. The main objective of the paper is to simulate a low-voltage ride-through (LVRT) control system that is convenient for the development of a controller that should have the ability to rectify fault signals. This paper proposes a novel method called grey wolf optimization with fuzzified error (GWFE) model to simulate the optimized control system. Further, it compares the GWFE-based LVRT system with the standard LVRT system, systems with minimum and maximum gain, and conventional methods like genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), ant bee colony (ABC), and grey wolf optimization (GWO) algorithms. Accordingly, it analyses the simulation results regarding qualitative analysis like active power, $V_{\mathrm{dc}}$ comparison, gain, pitch degree, reactive power, rotor current, stator current, and $V_{\mathrm{dc}}$ and $V_{\mathrm{dqs}}$ measurements; and quantitative analysis like RMSE computation of $V_{\mathrm{dc}}$ with varying speed. Hence, the proposed GWFE algorithm is beneficial for simulating the LVRT system compared to other conventional methods.