Abstract
This paper presents an intelligent controller employing Adaptive Neuro-Fuzzy Inference System (ANFIS) for extracting maximum power from the wind energy conversion system even during the change in the wind speed conditions with improved quality of power. The proposed induction generator with multilevel inverter along with intelligent controller based Maximum Power Point Tracking (MPPT) technique aims at integrating winds system with improved maximum power injection and minimum harmonic issues. The proposed method will improve the power quality which is delivered to the grid in terms of harmonic, and inject the maximum power to the grid. To validate the effectiveness of the proposed control strategy, ANFIS controller, Fuzzy Inference System (FIS) and without MPPT controller have been presented and tested using MATLAB/Simulink environment.
Highlights
In the last decade, great increase has been witnessed by the use of renewable energy due to the exhaustion of fossil fuels and different policies of industrial countries with the aim of reducing air pollution [1]
Power electronics researchers have been working in multilevel inverters, because of the following features such as: higher voltage operating capability, reduced rate of change of voltage, lower common mode voltages, reduced harmonic content, producing near sinusoidal current, filter of less operation, reduced switching and conduction loss by operating the power switches by fundamental switching which results in increased conversion efficiency
IG current and voltage are applied to Adaptive Neuro-Fuzzy Inference System (ANFIS) and the control output is obtained
Summary
Great increase has been witnessed by the use of renewable energy due to the exhaustion of fossil fuels and different policies of industrial countries with the aim of reducing air pollution [1]. Two serious problems associated with the above said controllers are speed efficiency trade off and poor quality output under rapid wind change. This can significantly deteriorate the performance of the grid connected wind energy system. The proposed scheme provides a better sinusoidal injection of current into the grid and extracts maximum power from the wind energy conversion system. It is verified with the comparison of other techniques such as fuzzy inference system and without a controller.
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