What question did this study set out to answer?

The central aim is to enhance the pitch-angle control strategy for wind energy systems using neural networks.

May 28, 2026Open Access

Dynamic performance enhancement of adjustable blade pitch angle for wind generation system applications based on artificial neural network control techniques

AAAsmaa G. AmeenSouth Valley University SMShuaiby MohamedHanbat National University GJGamal T. Abdel- JaberSouth Valley University

Key Points

The central aim is to enhance the pitch-angle control strategy for wind energy systems using neural networks.
Examined standard PID and Fractional PID control strategies.
Integrated neural network architectures like MLFFNN, CFNN, and Elman NN into the control systems.
Conducted simulations using MATLAB/Simulink to compare performances.
Achieved a minimum Mean Square Error of 0.0027024 with MLFFNN architecture.
Attained 98.9% power performance efficiency under various wind speed variations.
Demonstrated consistent superior performance of NN controllers over conventional PID and FPID systems.

Abstract

Abstract The increasing reliance on the renewable energy, particularly wind power, introduces significant challenges for modern power systems and can compromise system stability. This study proposes an improved pitch-angle control strategy for a 1.5 MW large-scale Wind Energy Conversion System (WECS) based on a Doubly-Fed Induction Generator (DFIG). To address the limitations of conventional controllers, which struggle with system nonlinearity and the requirement for highly accurate mathematical models, this study examined Proportional-Integral-Derivative (PID) and Fractional PID (FPID) strategies. These were integrated with Neural Network (NN) architectures, specifically Multilayer Feedforward (MLFFNN), Cascade Forward (CFNN), and Elman NN, to improve control performance. The results, using MATLAB/Simulink, show that the MLFFNN architecture provides superior performance. With a minimum Mean Square Error of 0.0027024 and a power performance efficiency reaching a 98.9% under step, ramp, and random wind speed variations, the proposed NN controller consistently outperforms both PID and FPID systems, offering a robust solution for large-scale wind energy applications.

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