基于机器学习的大型风力发电机温升影响因素分析

张辉; 崔权维; 周建星; 吴安

doi:10.19912/j.0254-0096.tynxb.2025-0668

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (9) : 705-716. DOI: 10.19912/j.0254-0096.tynxb.2025-0668

基于机器学习的大型风力发电机温升影响因素分析

张辉¹, 崔权维¹, 周建星¹, 吴安²

作者信息 +

MACHINE LEARNING-BASED ANALYSIS OF FACTORS INFLUENCING TEMPERATURE RISE OF LARGE-SCALE WIND TURBINE GENERATORS

Zhang Hui¹, Cui Quanwei¹, Zhou Jianxing¹, Wu An²

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文章历史 +

摘要

针对大容量永磁同步风力发电机温升加剧引发的绝缘老化与永磁体退磁等问题，以一台2 MW永磁同步风力发电机为研究对象，结合电磁损耗与风摩损耗结果对发电机温度场进行仿真分析。等效热网络法与单向磁-热耦合模型均存在较大误差，且忽略多因素交互影响，因此构建双向磁-热耦合模型，并引入线性回归模型以及随机森林模型，从而实现电机温度场的准确计算以及绕组温升影响因素的定量评估与动态预测。结果表明：随机森林模型可有效捕捉风速、转速及环境温度与绕组温度的非线性关系；转速与绕组温升呈现强正相关性，风速影响次之，环境温度影响最弱；绕组温度变化对各影响因素存在滞后效应，对风速响应速度最快，转速次之，环境温度最慢。经多方法研究对比表明，随机森林模型预测性能显著优于线性回归模型，双向磁-热耦合仿真结果与随机森林模型预测结果接近，单向磁-热耦合与等效热网络法计算精度相对较低。

Abstract

In large-capacity permanent magnet synchronous wind generators （PMSGs）, excessive temperature rise induces critical challenges such as insulation degradation and irreversible demagnetization of permanent magnets. This study investigates a 2 MW PMSG and calculates its temperature distribution based on electromagnetic and windage loss coupling analysis. Results indicate that the equivalent thermal network method and unidirectional magneto-thermal coupling model produce considerable calculation errors due to the neglect of multi-factor interactions. To solve this problem, a bidirectional magneto-thermal coupling model is developed, incorporating linear regression and random forest algorithms for accurate temperature prediction and sensitivity analysis. The random forest model demonstrates superior capability in characterizing the nonlinear relationship between winding temperature and influencing factors including wind speed, rotational speed and ambient temperature. Among these factors, rotational speed exhibits the strongest positive correlation with winding temperature rise, followed by wind speed, while ambient temperature has the least influence. Moreover, the variation of winding temperature shows a delayed response, with the shortest latency for wind speed variations, followed by rotational speed changes, and the slowest to ambient temperature fluctuations. Comparative analysis confirms that the random forest model outperforms the linear regression model in prediction accuracy, and the bidirectional coupling simulations align closely with its predictions, whereas unidirectional coupling and thermal network methods show lower accuracy.

导出引用

张辉, 崔权维, 周建星, 吴安. 基于机器学习的大型风力发电机温升影响因素分析[J]. 太阳能学报. 2026, 47(9): 705-716 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0668

Zhang Hui,Cui Quanwei,Zhou Jianxing,Wu An. MACHINE LEARNING-BASED ANALYSIS OF FACTORS INFLUENCING TEMPERATURE RISE OF LARGE-SCALE WIND TURBINE GENERATORS[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 705-716 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0668

中图分类号： TK81

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