Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data
Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data
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DOI：
10.1016/j.renene.2017.06.089
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发表时间：
2018-02-01
期刊：
影响因子：
8.7
通讯作者：
Uhl, Tadeusz
中科院分区：
文献类型：
--
作者：
Dao, Phong B.;Staszewski, Wieslaw J.;Uhl, Tadeusz
本文提出了一种新的方法，基于协整分析的监控和数据采集（SCADA）数据的状态监测和故障诊断的风力发电机组。从风力涡轮机数据的协整过程获得的协整残差的分析用于操作条件监测和自动故障和/或异常条件检测。所提出的方法进行了验证，使用的实验数据从风力涡轮机传动系统的标称功率为2兆瓦，在不同的环境和操作条件下。一个两阶段的协整为基础的程序进行六个过程参数的风力涡轮机，其中的数据趋势具有非线性特性。该方法进行了测试，使用两个案例研究与已知的故障。结果表明，该方法能有效地分析非线性数据趋势，连续监测风力涡轮机，可靠地检测异常问题。(C)2017爱思唯尔有限公司版权所有
This paper presents a new methodology based on cointegration analysis of Supervisory Control And Data Acquisition (SCADA) data for condition monitoring and fault diagnosis of wind turbines. Analysis of cointegration residuals obtained from cointegration process of wind turbine data is used for operational condition monitoring and automated fault and/or abnormal condition detection. The proposed method is validated using the experimental data acquired from a wind turbine drivetrain with a nominal power of 2 MW under varying environmental and operational conditions. A two-stage cointegration-based procedure is performed on six process parameters of the wind turbine, where data trends have nonlinear characteristics. The method is tested using two case studies with known faults. The results demonstrate that the proposed method can effectively analyse nonlinear data trends, continuously monitor the wind turbine and reliably detect abnormal problems. (C) 2017 Elsevier Ltd. All rights reserved.