Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data
Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data
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DOI：
10.3390/en14206601
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发表时间：
2021-10
期刊：
影响因子：
3.2
通讯作者：
C. McKinnon;James R Carroll;A. McDonald;S. Koukoura;C. Plumley
中科院分区：
文献类型：
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作者：
C. McKinnon;James R Carroll;A. McDonald;S. Koukoura;C. Plumley
风力涡轮机变桨系统的状态监测是一个活跃的研究领域，本文研究了使用隔离森林机器学习模型和监控和数据采集系统的数据，这一任务。本文研究了两个案例研究，涡轮机与液压或电动变桨系统，并使用隔离森林提前预测故障。这种新技术比较了每个涡轮机的几个模型，每个模型都使用不同的月数数据进行训练。比较了三种不同时间序列窗口长度的异常比例，以观察失效前的趋势和峰值。对这两种情况进行了比较，发现该技术可以在所有不正常涡轮机的液压和电动变桨系统发生故障前大约12至18个月检测到异常活动，并且可以在故障发生前立即发现异常上升的趋势。这些异常行为的高峰可能表明未来的故障，这将允许安排现场维护。因此，该方法可以改善桨距系统的计划维护活动的调度，而不管所采用的桨距系统。
Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.