Abstract
Abstract. Analysis of data from wind turbine supervisory control and data acquisition (SCADA) systems has attracted considerable research interest in recent years. Its predominant application is to monitor turbine condition without the need for additional sensing equipment. Most approaches apply semi-supervised anomaly detection methods, also called normal behaviour models, that require clean training data sets to establish healthy component baseline models. In practice, however, the presence of change points induced by malfunctions or maintenance actions poses a major challenge. Even though this problem is well described in literature, this contribution is the first to systematically evaluate and address the issue. A total of 600 signals from 33 turbines are analysed over an operational period of more than 2 years. During this time one-third of the signals were affected by change points, which highlights the necessity of an automated detection method. Kernel-based change-point detection methods have shown promising results in similar settings. We, therefore, introduce an appropriate SCADA data preprocessing procedure to ensure their feasibility and conduct comprehensive comparisons across several hyperparameter choices. The results show that the combination of Laplace kernels with a newly introduced bandwidth and regularisation-penalty selection heuristic robustly outperforms existing methods. More than 90 % of the signals were classified correctly regarding the presence or absence of change points, resulting in an F1 score of 0.86. For an automated change-point-free sequence selection, the most severe 60 % of all change points (CPs) could be automatically removed with a precision of more than 0.96 and therefore without any significant loss of training data. These results indicate that the algorithm can be a meaningful step towards automated SCADA data preprocessing, which is key for data-driven methods to reach their full potential. The algorithm is open source and its implementation in Python is publicly available.
Highlights
Wind energy plays a major role in the decarbonisation of energy systems around the world
The general advantage of cost-based penalties is preserved with F1 scores approximately 15 % above the best slope-heuristic results which are achieved at αslope2 = 11.5 and αslope1 = 4
A total of 600 signals from 33 turbines were analysed for an operational period of more than 2 years. During this time one-third of the signals showed one or more significant changes in behaviour induced by sensor and component malfunctions or maintenance actions. This finding highlights the need for an automated change points (CPs) detection method
Summary
Wind energy plays a major role in the decarbonisation of energy systems around the world. It has developed into a mature technology over the past decades and its levelised cost of electricity (LCOE) has reached a competitive level (IRENA, 2019). An effective strategy to further reduce O&M costs is to switch from a scheduled maintenance scheme to condition-based maintenance. Under such a scheme maintenance decisions are based on information about the turbine’s actual condition rather than on periodic inspections. Utilisation of operational SCADA data for CM has attracted considerable re-
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