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Model Predictive Pitch Control of PMSG-Based WECS Using Fuzzy-MPC and Deep Learning-Based Wind Prediction.

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Title: Model Predictive Pitch Control of PMSG-Based WECS Using Fuzzy-MPC and Deep Learning-Based Wind Prediction.
Authors: MUTHARASU, S.1 smutharasu12@gmail.com, THENMALAR, K.2 thenmalark@gmail.com
Source: Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p1021-1028. 8p.
Subjects: Wind energy conversion systems, Predictive control systems, Deep learning, Recurrent neural networks, Wind forecasting, Fuzzy systems, Permanent magnet generators
Abstract: Maintaining steady output power in Wind Energy Conversion Systems (WECS) under varying wind speeds, especially above the rated wind velocity, poses a significant control challenge. To address this, a Model Predictive Control (MPC) approach for pitch angle regulation in WECS is proposed. The nonlinear dynamics of the wind turbine are first linearized and formulated using the Takagi-Sugeno (T-S) fuzzy modelling technique, allowing effective handling of system nonlinearity. The fuzzy-based linearized model is then integrated into the MPC framework, which incorporates both pitch angle and generator torque constraints to ensure safe and optimal turbine operation. To eliminate the reliance on physical wind speed sensors and enhance control performance, a Recurrent Neural Network (RNN) model is developed to predict future wind velocity based on turbine dynamics. This model uses Long Short-Term Memory (LSTM) cells to retain temporal patterns over extended periods, significantly improving the wind speed forecasting accuracy. The integration of this deep learning-based wind prediction into the MPC enhances robustness and adaptability to fluctuating wind conditions. The effectiveness of the proposed predictive pitch angle control strategy is validated on a Permanent Magnet Synchronous Generator (PMSG)-based WECS. Simulation results demonstrate that the proposed method improves power output stability and control responsiveness while reducing mechanical stress on the turbine components. This hybrid approach combining fuzzy modelling, predictive control, and deep learning offers a promising solution for advanced wind turbine control under real-world dynamic conditions. [ABSTRACT FROM AUTHOR]
Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Model Predictive Pitch Control of PMSG-Based WECS Using Fuzzy-MPC and Deep Learning-Based Wind Prediction.
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  Data: <searchLink fieldCode="DE" term="%22Wind+energy+conversion+systems%22">Wind energy conversion systems</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+systems%22">Fuzzy systems</searchLink><br /><searchLink fieldCode="DE" term="%22Permanent+magnet+generators%22">Permanent magnet generators</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Maintaining steady output power in Wind Energy Conversion Systems (WECS) under varying wind speeds, especially above the rated wind velocity, poses a significant control challenge. To address this, a Model Predictive Control (MPC) approach for pitch angle regulation in WECS is proposed. The nonlinear dynamics of the wind turbine are first linearized and formulated using the Takagi-Sugeno (T-S) fuzzy modelling technique, allowing effective handling of system nonlinearity. The fuzzy-based linearized model is then integrated into the MPC framework, which incorporates both pitch angle and generator torque constraints to ensure safe and optimal turbine operation. To eliminate the reliance on physical wind speed sensors and enhance control performance, a Recurrent Neural Network (RNN) model is developed to predict future wind velocity based on turbine dynamics. This model uses Long Short-Term Memory (LSTM) cells to retain temporal patterns over extended periods, significantly improving the wind speed forecasting accuracy. The integration of this deep learning-based wind prediction into the MPC enhances robustness and adaptability to fluctuating wind conditions. The effectiveness of the proposed predictive pitch angle control strategy is validated on a Permanent Magnet Synchronous Generator (PMSG)-based WECS. Simulation results demonstrate that the proposed method improves power output stability and control responsiveness while reducing mechanical stress on the turbine components. This hybrid approach combining fuzzy modelling, predictive control, and deep learning offers a promising solution for advanced wind turbine control under real-world dynamic conditions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.17559/TV-20250612002742
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        Text: English
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        Type: general
      – SubjectFull: Predictive control systems
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Recurrent neural networks
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      – SubjectFull: Wind forecasting
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      – SubjectFull: Fuzzy systems
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      – SubjectFull: Permanent magnet generators
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      – TitleFull: Model Predictive Pitch Control of PMSG-Based WECS Using Fuzzy-MPC and Deep Learning-Based Wind Prediction.
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            NameFull: MUTHARASU, S.
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            – D: 01
              M: 05
              Text: 2026
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              Y: 2026
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