Adaptive Fuzzy Logic Control to Enhance Pitch Angle Controller for Variable-Speed Wind Turbines

Van, Tan Luong; Dang, Ngoc Khoa; Doan, Xuan Nam; Truong, Trung Hieu; Minh, Ho Nhut

doi:10.1109/kse.2018.8573332

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…Furthermore, the fuzzy logic control (FLC) was applied in the pitch angle control. Reference [24] proposes the FLC-PI pitch angle controller. Both the mentioned paper [24] and our paper discuss the control strategies for wind turbine systems using FLC, but they focused on different aspects.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [24] proposes the FLC-PI pitch angle controller. Both the mentioned paper [24] and our paper discuss the control strategies for wind turbine systems using FLC, but they focused on different aspects. The key difference lies in the control focuses, where [24] focused on smoothing performance DFIG wind turbine operation utilizing FLC pitch angle control, while this study aimed to enhance the frequency regulation of VSWTs.…”

Section: Introductionmentioning

confidence: 99%

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Novel Fuzzy Logic Controls to Enhance Dynamic Frequency Control and Pitch Angle Regulation in Variable-Speed Wind Turbines

Phung,

Wu,

Pham

2024

Energies

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This study introduced a novel control approach based on fuzzy logic control (FLC) to enhance the frequency regulation capacity of variable-speed wind turbines (VSWTs). The proposed method integrates FLC within droop and inertia control loops. Real-time measurements of the system frequency and the rate of change of frequency (ROCOF) serve as inputs to the FLC, enabling the method to improve the frequency response by VSWTs. In addition, the method employs FLC for pitch angle frequency control, optimizing reserve power for frequency regulation under varying wind speed levels. The innovative aspect of this study lies in the simultaneous application of FLC to pitch angle frequency control and droop/inertia control, leading to the enhanced frequency regulation capability of VSWTs and smoother operation across a range of wind speeds. Compared with traditional methods, the proposed approach provides a comprehensive and effective solution to the challenges associated with frequency regulation in VSWTs. Through simulations across different wind speed scenarios, the proposed control method demonstrated the best performance among various mature methods, highlighting the efficacy of the proposed method on the frequency regulation of VSWTs under different wind speeds. This study’s findings highlight the potential of the proposed FLC-based method to optimize frequency regulation and contribute to more reliable and efficient wind energy systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Fuzzy Logic Controls to Enhance Dynamic Frequency Control and Pitch Angle Regulation in Variable-Speed Wind Turbines

Phung,

Wu,

Pham

2024

Energies

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“…As well, structural cascade control [13] and the control fault tolerant [17] are used in compensating unknown time-varying nonlinearity and disturbances. Some other available techniques are observer-based blade pitch control [18] and fuzzy logic control [19][20][21]. In addition, pitch angle control has been improved through model predictive control (MPC) approach, but with related prediction computing complexity [22].…”

Section: Introductionmentioning

confidence: 99%

“…Another way by which pitch control has been improved is that instead of relying on prediction, the control is accomplished using a future knowledge of wind conduct and the wind speed data sent to WTG from one another [23]. Such signal may then be provided for the MPC to ensure best possible response [18][19]. Information on future wind conditions aids MPC to provide optimal solutions while considering system constraints [20].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power Generation

Ajewole,

Agboola,

Hassan

et al. 2023

JD-FEWS

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Presented in this study is an artificial intelligence approach to pitch angle control in wind turbines for the enhancement of the power generation efficiency of wind energy conversion systems. A two-input neural network model was developed and trained using backward propagation technique to adjust the pitch angle of the turbine in response to the speed of turbine generator and the rate of change of the speed. Ten-year real-life data on the wind speeds of a study location was used to validate the approach. It was found that the method performs well in controlling the mechanical power developed by the turbine above the turbine’s rated wind speed, and with fast processing time. 44.44%improvement was achieved in the mechanical power developed by the turbine. The control approach is thus recommended for the effective management of wind energy conversion systems towards enhancing availability and reliability of electric power supply.

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“…To compensate for the unknown time-varying nonlinearities and disturbances various control structures has been used such as nonlinear PI (N-PI) [8], fractional PI [9], cascade control structure [10] and Fault-tolerant control (FTU) to Compensate for the pitch actuator fault [11]. Other strategies to enhance the pitch control include; fuzzy logic control (FLC) [12][13] [14], observer-based bladepitch controller [15] and finite-frequency identification PI controller [16].…”

Section: Introductionmentioning

confidence: 99%

A New Pitch Angle Control Method of Wind Turbine Generators Based on Feedforward Wind Speed Information

Sahib

Nayl

2019

E3S Web Conf.

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In this work, a new strategy to control the pitch angle of wind turbine generator is proposed. The strategy is based on designing an intelligent control system capable of maintaining a stable minimum fluctuating power generation. This can be achieved by providing the wind speed information to the controller in advance and hence allowing the controller to take the optimum action in controlling the blade pitch angle. A model based optimizer uses Model Predictive Control (MPC) technique to predict the wind turbine generator future behaviour and select the optimal control actions assisted by the wind speed information while satisfying the power generation constraints. The simulation results show that a significant improvement can be made using the proposed control method.

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Adaptive Fuzzy Logic Control to Enhance Pitch Angle Controller for Variable-Speed Wind Turbines

Cited by 4 publications

References 14 publications

Novel Fuzzy Logic Controls to Enhance Dynamic Frequency Control and Pitch Angle Regulation in Variable-Speed Wind Turbines

Novel Fuzzy Logic Controls to Enhance Dynamic Frequency Control and Pitch Angle Regulation in Variable-Speed Wind Turbines

Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power Generation

A New Pitch Angle Control Method of Wind Turbine Generators Based on Feedforward Wind Speed Information

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