Abstract
Abstract. The cointegration method has recently attracted a growing interest from scientists and engineers as a promising tool for the development of wind turbine condition monitoring systems. This paper presents a short review of cointegration-based techniques developed for condition monitoring and fault detection of wind turbines. In all reported applications, cointegration residuals are used in control charts for condition monitoring and early failure detection. This is known as the residual-based control chart approach. Vibration signals and SCADA data are typically used with cointegration in these applications. This is due to the fact that vibration-based condition monitoring is one of the most common and effective techniques (used for wind turbines); and the use of SCADA data for condition monitoring and fault detection of wind turbines has become more and more popular in recent years.
Highlights
In recent years, with the fast development of wind power technology, the number and capacity of wind turbines (WTs) have rapidly increased
This paper presents a short review of cointegration-based techniques developed for condition monitoring and fault detection of wind turbines in order to demonstrate the state-of-art development of the approach
It is suggested that if gearbox vibration signals of a wind turbine are combined with its supervisory control and data acquisition (SCADA) data for cointegration analysis, earlier fault prediction can be achieved with high accuracy
Summary
With the fast development of wind power technology, the number and capacity of wind turbines (WTs) have rapidly increased. The work in [12,13,14] presented a novel data analysis/processing method – based on the concept of residual-based control chart – for condition monitoring and fault diagnosis of wind turbines (WTs). The results have demonstrated that cointegration can be used to successfully detect the introduced damages under conditions not allowing for direct discrimination between damage and EOVs. It should be noted here that all applications – reported in [8,9,10] for SHM systems and in [1220] for wind turbine condition monitoring – have used the linear cointegration theory that was originally developed in [6, 7] and intimately connected with the concept of linear error correction models. An approximately homoscedastic nonlinear cointegration method has been proposed for the removal of undesired (environmental, operational or measurement) trends from SHM data in general and wind turbine SCADA data in particular. The method has been successfully applied for condition monitoring and fault detection of a wind turbine drivetrain with a nominal power of 2 MW in the presence of nonlinearity between operational parameters
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