Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data

被引：148

作者：

Dao, Phong B. ^{[1
]}

Staszewski, Wieslaw J. ^{[1
]}

Barszcz, Tomasz ^{[1
]}

Uhl, Tadeusz ^{[1
]}

机构：

[1] AGH Univ Sci & Technol, Dept Robot & Mechatron, Al Mickiewicza 30, PL-30059 Krakow, Poland

来源：

RENEWABLE ENERGY | 2018年 / 116卷

关键词：

Wind turbine; Condition monitoring; Fault detection; Cointegration; SCADA; Trend analysis; DAMAGE DETECTION; DIAGNOSIS; SYSTEM;

D O I：

10.1016/j.renene.2017.06.089

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页码：107 / 122

页数：16