风力轮机驱动线的异常检测基于序列分解 交互式网络交互式网络
Qiucheng Lyu1, Yuwei He1, Shijing Wu1,2
1School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
一种新的方法,分解序列交互网络 (DSI-Net),使用SCADA数据准确检测风力轮机 (WT) 的健康异常. 这种方法通过分析时空特征来改善现有模型,以可靠地识别异常.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 风力轮机通过监督控制和数据采集 (SCADA) 系统产生关键数据.
- 准确的健康状况识别对于高效的风能生产和维护至关重要.
- 现有的异常检测方法可能无法充分利用SCADA数据中的时空信息.
研究的目的:
- 提出一种新的异常检测方法,即分解序列交互网络 (DSI-Net),用于识别风力轮机的健康状态.
- 从SCADA数据中有效利用全面的时空信息,以增强异常检测.
- 提高风力轮机健康监测的准确性和可靠性.
主要方法:
- 一个DSI-Net模型被训练在从健康的风力轮机中预处理的SCADA数据上.
- DSI-Net通过交互式学习过程提取潜在的时空特征,将数据分解为趋势和季节性次序.
- 在线数据被处理以计算残余值,然后使用指数加权移动平均值 (EWMA) 分析残余值和根平均平方误差 (RMSE) 来识别异常.
主要成果:
- DSI-Net模型成功地从SCADA数据中识别了潜在的时空特征.
- 由EWMA处理的残留物和RMSE有效地标志着风力轮机运行中的异常.
- 控制实验表明,DSI-Net在准确性和有效性方面优于现有的异常检测模型.
结论:
- 使用SCADA数据,DSI-Net提供了一种有效和准确的风力轮机异常检测方法.
- 该模型捕捉时空特征的能力提高了风力轮机健康状况的识别.
- 这种方法为风能系统的条件监测提供了显著的进步.
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