Health Assessment Methods for Wind Turbines Based on Power Prediction and Mahalanobis Distance
| Autor: | Jun Zhan, Ronglin Wang, Xie Zhengjuan, Wang Yaguo, Lingzhi Yi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mahalanobis distance
Wind power business.industry Operational maintenance 020206 networking & telecommunications 02 engineering and technology Turbine Power (physics) Status assessment Health assessment Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Environmental science 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Great Relation Artificial intelligence business Software Marine engineering |
| Zdroj: | International Journal of Pattern Recognition and Artificial Intelligence. 33:1951001 |
| ISSN: | 1793-6381 0218-0014 |
| DOI: | 10.1142/s0218001419510017 |
| Popis: | The output power of wind turbine has great relation with its health state, and the health status assessment for wind turbines influences operational maintenance and economic benefit of wind farm. Aiming at the current problem that the health status for the whole machine in wind farm is hard to get accurately, in this paper, we propose a health status assessment method in order to assess and predict the health status of the whole wind turbine, which is based on the power prediction and Mahalanobis distance (MD). Firstly, on the basis of Bates theory, the scientific analysis for historical data from SCADA system in wind farm explains the relation between wind power and running states of wind turbines. Secondly, the active power prediction model is utilized to obtain the power forecasting value under the health status of wind turbines. And the difference between the forecasting value and actual value constructs the standard residual set which is seen as the benchmark of health status assessment for wind turbines. In the process of assessment, the test set residual is gained by network model. The MD is calculated by the test residual set and normal residual set and then normalized as the health status assessment value of wind turbines. This method innovatively constructs evaluation index which can reflect the electricity generating performance of wind turbines rapidly and precisely. So it effectively avoids the defect that the existing methods are generally and easily influenced by subjective consciousness. Finally, SCADA system data in one wind farm of Fujian province has been used to verify this method. The results indicate that this new method can make effective assessment for the health status variation trend of wind turbines and provide new means for fault warning of wind turbines. |
| Databáze: | OpenAIRE |
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