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25 April 2023 Health assessment of wind turbines based on output power modelling
Xiaoyan Yin
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Proceedings Volume 12598, Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022); 1259819 (2023) https://doi.org/10.1117/12.2673000
Event: Eighth International Conference on Energy Materials and Electrical Engineering (ICMEE 2022), 2022, Guangzhou, China
Abstract
Machine learning based performance assessment of wind turbines using the widely available SCADA data has been receiving growing interest. This work proposes a pragmatic health evaluation approach of wind turbines based on output power modelling. For that, random forests regression models to predict output power are trained respectively by ‘normal’ and ‘under-performance’ samples. The trained two models are used to predict output power of testing data. By comparing the absolute values of the residuals of testing data, the working state is identified at each time point. A health index, which is based on the percentiles of ‘normal’ samples in a defined period, is introduced to evaluate the health condition of the wind turbine. The degradation of the HI can give an early warning of the wind turbine. The proposed approach successfully give an early warning for the potential fault around 6 hours prior the traditional SCADA system.
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Xiaoyan Yin "Health assessment of wind turbines based on output power modelling", Proc. SPIE 12598, Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022), 1259819 (25 April 2023); https://doi.org/10.1117/12.2673000
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KEYWORDS
Wind turbine technology
Data modeling
Random forests
Machine learning
Modeling
Renewable energy
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