Abstract
Premature failures caused by excessive wear are responsible for a large fraction of the maintenance costs of wind turbines. Therefore, it is crucial to be able to identify the propagation of these failures as early as possible. To this end, a novel condition monitoring method is proposed that uses statistical data analysis techniques and machine learning to construct a multivariate anomaly detection framework, based on high-frequency temperature SCADA data from wind turbines. This framework contains several steps. First, there is a preprocessing step in which relevant wind turbine states are extracted from the data. These states are the operating conditions and whether or not the turbine exhibits transient behavior. The second step entails anomaly detection on the temperature time series data. Fleet information is used to filter out exogenous (environmental) factors. Furthermore, multiple models are combined to get more stable and robust anomaly detections. By combining them the weaknesses of the individual models are alleviated resulting in a better overall performance. A limitation of machine learning-based anomaly detection on temperature data is the requirement that at least one year of “healthy” (meaning without anomalies) training data is available to account for seasonal effects. The lack of verified “healthy” data spread out evenly over the seasons generally means that the anomaly detection accuracy is severely compromised for the unrepresented seasons. This research uses smart retraining to reduce this limitation. Statistical techniques that leverage the information of the fleet are used to extract “healthy” data from at least one year, but preferably multiple years, of unverified data. This can then be used as training data for the machine learning-based models. To validate the pipeline, temperature and failure data of a real operational wind farm is used. Although the methodology is general in its scope, the validation case focusses specifically on generator bearing failures.
Highlights
Under the impulse of a global shift towards renewable energy production, there are currently large investments happening in the wind turbine industry
In this research we present a framework that is based on more traditional statistical techniques like Autoregressive Integrated Moving Average (ARIMA), Ordinary Least Squares (OLS), cumulative sum control (CUSUM) charts, etc
Because a replacement changes something structurally to the data the model is reset at the beginning of each run. This implies that a new ARIMA model is learned based on 6 months of information, a new OLS is fit and the CUSUM is reset to 0
Summary
Under the impulse of a global shift towards renewable energy production, there are currently large investments happening in the wind turbine industry. Recent studies have shown that the operation and maintenance of wind turbines accounts for 25-40% of the levelized cost of energy (Pfaffel, Faulstich & Rohrig, 2017). These costs are driven in part by premature failures caused by excessive wear on components. Being able to predict the failure of a component plays an important part in reducing the operational costs of wind turbines, since it can avoid unnecessary downtimes. This in turn can give a boost to the profitability of the industry
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