Abstract
Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides promising insights into sensor fault detection due to the accessibility of the data and the abundance of sensor information. However, SCADA data are essentially multivariate time series with inherent spatio-temporal correlation characteristics, which has not been well considered in the existing wind turbine fault detection research. This paper proposes a novel classification-based fault detection method for wind turbine sensors. To better capture the spatio-temporal characteristics hidden in SCADA data, a multiscale spatio-temporal convolutional deep belief network (MSTCDBN) was developed to perform feature learning and classification to fulfill the sensor fault detection. A major superiority of the proposed method is that it can not only learn the spatial correlation information between several different variables but also capture the temporal characteristics of each variable. Furthermore, this method with multiscale learning capability can excavate interactive characteristics between variables at different scales of filters. A generic wind turbine benchmark model was used to evaluate the proposed approach. The comparative results demonstrate that the proposed method can significantly enhance the fault detection performance.
Highlights
Wind energy as an inexhaustible and fast-growing clean renewable energy source has received considerable attention
This paper proposes a novel classification-based fault detection method for wind turbine sensors
The advantage of the proposed multiscale spatio-temporal convolutional deep belief network (MSTCDBN) fault detection method was proved by comparing with traditional convolutional deep belief network (CDBN) and its other variants
Summary
Wind energy as an inexhaustible and fast-growing clean renewable energy source has received considerable attention. Due to the availability and economy of SCADA data, sensor fault detection based on SCADA data has been considered as a feasible and valuable method, and, until now, numerous analysis approaches have been extensively studied in the literature These methods mainly include artificial neural networks (ANN) [10,11], power curve-based method [12,13], support vector machine [14,15,16], and classifier fusion-based method [17]. A novel MSTCDBN method is proposed to overcome the limitations of traditional CDBN that lack the ability to capture the spatio-temporal correlations inherent in multivariate time series and cannot realize multiscale feature learning. The proposed the multiscale spatio-temporal proposed method in the faultus detection of wind turbine sensors, and comparative feature learning ability enables to enhance the classification performance greatly. A systematic description of the experiment and the acquisition and standard CDBN and introduces the proposed MSTCDBN approach for fault detection of wind turbine preprocessing multivariate time series signals are proposed
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