Optimal Tuning of a New Multi-input Multi-output Fuzzy Controller for Doubly Fed Induction Generator

What is it about?

This paper presents an optimal design and tuning of fuzzy logic controllers (FLC) for a 1.5-MW doubly-fed induction generator (DFIG), grid-connected, wind energy conversion system (WECS) using intelligent methodologies such as particle swarm optimizer (PSO), the gray wolf optimization (GWO), moth-flame optimizer (MFO), and multi-verse optimizer (MVO). FLC scaling factors are optimized for both dc-link voltage controller and current regulators of the grid-side converter and rotor-side converter of the back to back of DFIG wind turbine.

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Photo by RawFilm on Unsplash

Photo by RawFilm on Unsplash

Why is it important?

A multi-objective optimization methodology is proposed which aims to minimize the steady-state errors of these controllers to improve the dynamic operation of the DFIG wind energy system subjected to variable wind speed conditions. Finally, a comparison is carried out between the different optimization techniques for FLC using PSO, GWO, MFO, and MVO, also between the proposed optimized controller and PI controller. The main contribution of this study is that it proposes a new control methodology for a DFIG-based WECS. This strategy is to optimize multi-input multi-output MIMO-FLC scaling factors by applying PSO, GWO, MFO, and MVO algorithms to control the d-q component of rotor and stator currents to control the active and reactive power of the DFIG.