Wind turbines abnormality detection through analysis of wind farm power curves. (November 2016)

• Record Type: Journal Article
• Authors: Wang, Shuangyuan; Huang, Yixiang; Li, Lin; Liu, Chengliang
• Is Part Of: Measurement. Volume 93(2016)
• Page Start: 178
• Page End: 188
• Publication Date: 2016-11
• Subjects: Abnormality detection -- Abnormality prediction -- Power curves -- Wind turbines -- Dissimilarity measures; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2016.07.006
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Proposed a hybrid method for abnormality detection using the power curve. Used a one-for-all graded statistics technique to find outliers of wind turbines. Dissimilarity measure is applied to estimate wind turbine downtime intervals. Abnormality is predicted using output power curves and downtime intervals. Abstract: Abnormality detection and prediction is a critical technique to identify wind turbine failures at an early stage, thus avoiding catastrophes. In this study, we propose a new abnormality detection and prediction technique based on heterogeneous signals and information, such as output power signals and wind turbines downtime event information collected from the supervisory control and data acquisition (SCADA) system. First, discriminant statistical feature extraction is performed on the power signals in both the time-domain and frequency-domain. Then, a sideband expression is derived for normalized statistical data based on quartiles. In addition, a dissimilarity metric is defined to calculate the distances between downtime time intervals, and a higher dimension feature space is obtained. To reduce the dimension of the feature space, the Laplacian Eigenmaps (LE) nonlinear dimensionality reduction method is implemented. Afterwards, a Linear Mixture Self-organizing Maps (LMSOM) classifier is applied to differentiate abnormal types and a cumulative trend difference method is utilized to predict the faults in wind turbine. The method is validated and applied to data collected from a wind farm in north China. The results show that the proposed technique can effectively detect and predict wind turbine abnormalities.