Novel Condition Monitoring Method for Wind Turbines Based on the Adaptive Multivariate Control Charts and SCADA Data

Qinkai Han,Tao Hu,Zhentang Wang

doi:10.1155/2020/8865776

Abstract

A novel condition monitoring method based on the adaptive multivariate control charts and the supervisory control and data acquisition (SCADA) system is developed. Two types of control charts are adopted: one is the adaptive exponential weighted moving average (AEWMA) control chart for abnormal state detection, and the other is the multivariate exponential weighted moving average (MEWMA) control chart for anomaly location determination. Optimization procedures for these control charts are implemented to achieve minimum out-of-control average running length. Multivariate regression analysis is utilized to obtain the normal condition prediction model of wind turbine with fault-free SCADA data. After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. Various tests on the wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with conventional control charts, the AEWMA control chart is more sensitive to the abnormal state and thus has a more effective anomaly identification ability and better robustness. It is shown that the MEWMA control chart combined with the out-of-limit number index can effectively locate and identify the abnormal component.

Highlights

With the increasing sustainable energy and environmental demands, wind energy has become one of the world’s fastest growing renewable and green energy sources
(iii) Various tests on a wind turbine with normal and abnormal states are conducted. e exact anomaly time and type are known from the alarm log; the performance and robustness of various control charts could be compared comprehensively
Multivariate regression analysis (MRA) is utilized to obtain the normal condition prediction models (NCPMs) of wind turbine with fault-free Supervisory control and data acquisition (SCADA) data

Summary

Introduction

With the increasing sustainable energy and environmental demands, wind energy has become one of the world’s fastest growing renewable and green energy sources. The EWMA control chart can provide greater sensitivity to small shifts, it is not as effective as the Shewhart chart, where the shifts in the process mean level are relatively large due to the inertia problem [37] In actual applications, such as monitoring of wind turbines, the shift of the residuals from the NCPM is unknown, which might cause the insufficiency of the EWMA control chart if the larger shift appears. There have been few attempts to comprehensively compare the performance and robustness of both EWMA and AEWMA control charts in monitoring the residuals from the NCPM of wind turbine SCADA data. Erefore, the MEWMA control chart is adopted to monitor the multidimensional SCADA data and to identify and locate the anomaly component of the wind turbine by analyzing the variation of Qij. 3. ARL1 values can be calculated for a given shift δ. en, the smoothing parameter r for which ARL1 is the smallest can be identified

MRA on Fault-Free SCADA Data

Wind Turbine CM System Based on AMCCs

CM Examples

Conclusions