| 周凌,黄倩,曾进辉,黄浪尘,龙霞飞.风电机组定子绕组温度传感器状态自确认研究[J].电子测量与仪器学报,2025,39(11):11-22 |
| 风电机组定子绕组温度传感器状态自确认研究 |
| Research on self-validation of temperature sensor status forstator winding of wind turbine |
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| DOI： |
| 中文关键词: 风电机组 温度传感器 自确认 信息融合技术 异常数据恢复 |
| 英文关键词:wind turbines temperature sensor self-validation information fusion technology restructure |
| 基金项目:国家自然科学基金（52377185）、湖南省教育厅优秀青年项目（22B0590，23B0537）的资助 |
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| Author | Institution |
| Zhou Ling | College of Electrical and Information Engineering, Hunan University of Technology,Zhuzhou 412002, China |
| Huang Qian | College of Electrical and Information Engineering, Hunan University of Technology,Zhuzhou 412002, China |
| Zeng Jinhui | College of Electrical and Information Engineering, Hunan University of Technology,Zhuzhou 412002, China |
| Huang Langchen | College of Electrical and Information Engineering, Hunan University of Technology,Zhuzhou 412002, China |
| Long Xiafei | College of Electrical and Information Engineering, Hunan University of Technology,Zhuzhou 412002, China |
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| 摘要点击次数: 368 |
| 全文下载次数: 332 |
| 中文摘要: |
| 针对提高智慧风场风电机组运行可靠性和传感器状态自确认问题,以风电机组定子绕组温度传感器为研究对象,提出了一种融合多源信息与智能算法的传感器状态自确认方法。首先，基于灰色关联分析理论，利用传感器的相关性和信息融合技术,通过计算某风场异常定子绕组温度传感器与同机同类传感器之间的灰色关联度,实现传感器异常状态识别。其次，利用皮尔逊相关性和专家系统判断,筛选出和定子绕组温度传感器关联性较强的参数,建立长短期记忆神经网络(long short-term memory, LSTM)多参数输入单输出异常数据恢复模型,并通过麻雀算法（sparrow search algorithm, SSA）对LSTM模型的超参数进行优化。为验证数据恢复模型的精度,通过模拟异常数据恢复表明该模型的精度达到了99.69%。最后对该定子绕组温度传感器异常数据进行了恢复,基于贝叶斯动态不确定度评估方法，对恢复数据进行置信度分析，从而实现对传感器状态的动态自确认。 |
| 英文摘要: |
| In order to improve the operational reliability of wind turbines in smart wind farms and the self-confirmation of sensor status, a novel self-validation method for sensor status is proposed using the stator winding temperature sensor of wind turbines as an example. First, based on grey relational analysis theory, and utilizing sensor correlation and information fusion technology, the grey correlation degree between the abnormal stator winding temperature sensor of a specific wind field and the same type of sensor on the same machine is calculated to achieve sensor anomaly state recognition. Second, using Pearson correlation and expert system judgment, parameters with strong correlation to the stator winding temperature sensor are identified. A long short-term memory (LSTM) multi-parameter input, single-output abnormal data reconstruction model is then established and optimized using the sparrow search algorithm (SSA) to improve the model’s accuracy. To verify the model’s reconstruction accuracy, simulations of abnormal data recovery showed that the accuracy reached 99.69%. Finally, the abnormal data of the stator winding temperature sensor was recovered, and the dynamic validation uncertainty of the recovered data was calculated using a Bayesian algorithm, achieving self-validation of the sensor’s state. |
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