Abstract
This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the stability of regression coefficients of the model on new observations, and detect a structural change in the form of coefficient instability using CUSUM tests. The method is applied for on-line condition monitoring of a WT using temperature-related SCADA data. A sequence of CUSUM test statistics is used as a damage-sensitive feature in a control chart scheme. If the sequence crosses either upper or lower critical line after some recursive regression iterations, then it indicates the occurrence of a fault in the WT. The method is validated using two case studies with known faults. The results show that the method can effectively monitor the WT and reliably detect abnormal problems.
Highlights
In econometrics and statistics, a structural break is an unexpected change over time in the parameters of regression models, which can lead to seriously biased estimates and forecasts and the unreliability of models [1,2,3]
The main ideas are to first form a multiple linear regression model using data collected while the system or structure of interest is operating in the normal state without fault, start monitoring the stability of the regression coefficients of the model on new arriving observations, and detect a structural change of the model using structural break tests
It should be mentioned that the same wind turbine supervisory control and data acquisition (SCADA) data set was used in the author’s previous paper [48], which is based on the cointegration analysis approach
Summary
A structural break is an unexpected change over time in the parameters of regression models, which can lead to seriously biased estimates and forecasts and the unreliability of models [1,2,3]. Macroeconomic and financial data are subject to occasional structural breaks, which can be caused by various economic and political events [5,6]. Chow developed an F-test for regime shift in parameters and resolved how to detect a single structural change by assuming that such break dates are known. The first group is the classical approach, which employs retrospective tests using a historical data set of a given length These tests are based on F statistics. The second group is the fluctuation-type test in a monitoring scheme Within this test framework, a regression relationship is known to be stable for a given history period; one will test whether incoming data are consistent with the previously established relationship [3,4]. Fluctuation tests do not assume a particular pattern of structural change
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