Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks

Lin, Zi, Liu, Xiaolei and Collu, Maurizio (2020) Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks. International Journal of Electrical Power & Energy Systems, 118. p. 105835. ISSN 0142-0615

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Official URL: https://doi.org/10.1016/j.ijepes.2020.105835

Abstract

Wind power plays a key role in reducing global carbon emission. The power curve provided by wind turbine manufacturers offers an effective way of presenting the global performance of wind turbines. However, due to the complicated dynamics nature of offshore wind turbines, and the harsh environment in which they are operating, wind power forecasting is challenging, but at the same time vital to enable condition monitoring (CM). Wind turbine power prediction, using supervisory control and data acquisition (SCADA) data, may not lead to the optimum control strategy as sensors may generate non-calibrated data due to degradation. To mitigate the adverse effects of outliers from SCADA data on wind power forecasting, this paper proposed a novel approach to perform power prediction using high-frequency SCADA data, based on isolate forest (IF) and deep learning neural networks. In the predictive model, wind speed, nacelle orientation, yaw error, blade pitch angle, and ambient temperature were considered as input features, while wind power is evaluated as the output feature. The deep learning model has been trained, tested, and validated against SCADA measurements. Compared against the conventional predictive model used for outlier detection, i.e. based on Gaussian Process (GP), the proposed integrated approach, which coupled IF and deep learning, is expected to be a more efficient tool for anomaly detection in wind power prediction.

Item Type: Article
Uncontrolled Keywords: Wind power prediction, Deep learning neural networks, Isolation forest, Outlier detection, Offshore wind turbines
Subjects: H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Elena Carlaw
Date Deposited: 23 Sep 2020 14:41
Last Modified: 23 Sep 2020 14:45
URI: http://nrl.northumbria.ac.uk/id/eprint/44230

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