报告详情
NN-based NMPC Control law approximation for Maximal Wind Energy Extraction in wind turbines
编号：9
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更新：2025-10-11 21:49:47 浏览：3次
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摘要
To reduce the computational burden of online Nonlinear Model Predictive Control (NMPC) for Maximum Wind Energy Extraction (MWEE) in wind turbines, this paper proposes a neural network-based approximation method for NMPC control laws. The study investigates the principles of MPC for maximum wind energy capture, designs the prediction model's input/output parameters and structure, analyzes the impact of training data characteristics (average wind speed, turbulence intensity(TI), prediction horizon) on approximation effectiveness, and provides data-driven support for optimal model selection through comparative analysis of different network architectures. Results demonstrate that turbulence intensity and average wind speed data significantly impact model performance, while prediction horizon data exhibits minimal influence. Training with mixed turbulence level data substantially enhances model generalization capability (achieving up to 19.5% RMSE reduction), whereas mixed prediction horizon data yields limited improvement (9.6% reduction). This study proposes a neural network-based MWEE-MPC control method, providing a novel approach for real-time efficient wind turbine control under complex wind conditions.
关键词
Maximum Wind Energy Extraction,Nonlinear Model Predictive Control,Neural Network Approximation,Control Law Approximation
报告人
非 谢
学生 中南大学稿件作者
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重要日期
-
会议日期
11月07日
2025
至11月09日
2025
-
10月12日 2025
初稿截稿日期
-
10月20日 2025
注册截止日期
主办单位
IEEE西南交通大学IAS学生分会
承办单位
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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