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Artificial neural network-based adaptive control for a DFIG-based WECS.

S Labdai¹, N Bounar², A Boulkroune²

¹National Polytechnic School of Algiers, LCP laboratory, 10 Av. Hassen Badi, BP 182, Algiers, Algeria.

ISA Transactions

|January 4, 2022

Summary

This summary is machine-generated.

This study introduces an artificial neural network adaptive control for wind energy systems. The method enhances power capture and grid stability, outperforming existing techniques.

Keywords:

Adaptive control Artificial neural networks Doubly-fed induction generator Wind energy conversion system

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Area of Science:

Electrical Engineering
Control Systems
Renewable Energy

Background:

Wind energy conversion systems (WECS) require advanced control for optimal performance.
Doubly-fed induction generators (DFIGs) are common in WECS but present control challenges due to nonlinearities.

Purpose of the Study:

To develop an artificial neural network (ANN)-based adaptive control for DFIG-based WECS.
To achieve maximum power extraction and regulate stator reactive power for grid compliance.

Main Methods:

Utilizing ANNs to estimate nonlinear system uncertainties.
Employing the Lyapunov method to ensure closed-loop system stability.
Comparing the proposed ANN control with vector control and sliding mode control.

Main Results:

The ANN-based adaptive control effectively extracts maximum wind power.
Stator reactive power is regulated according to grid requirements.
Simulations demonstrate superior performance compared to vector and sliding mode control.

Conclusions:

The proposed ANN adaptive control is effective for DFIG-based WECS.
This approach enhances both power generation efficiency and grid integration.
The method offers a robust alternative to conventional control strategies.