a short-term outage model of wind turbines with doubly fed induction generators based on supervisory control and data acquisition data

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ID: 172660
2016
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Abstract
This paper presents a short-term wind turbine (WT) outage model based on the data collected from a wind farm supervisory control and data acquisition (SCADA) system. Neural networks (NNs) are used to establish prediction models of the WT condition parameters that are dependent on environmental conditions such as ambient temperature and wind speed. The prediction error distributions are discussed and used to calculate probabilities of the operation of protection relays (POPRs) that were caused by the threshold exceedance of the environmentally sensitive parameters. The POPRs for other condition parameters are based on the setting time of the operation of protection relays. The union probability method is used to integrate the probabilities of operation of each protection relay to predict the WT short term outage probability. The proposed method has been used for real 1.5 MW WTs with doubly fed induction generators (DFIGs). The results show that the proposed method is more effective in WT outage probability prediction than traditional methods.
Reference Key
sun2016energiesa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Peng Sun;Jian Li;Junsheng Chen;Xiao Lei
Journal acs combinatorial science
Year 2016
DOI
10.3390/en9110882
URL
http://www.mdpi.com/1996-1073/9/11/882
Keywords
prediction model comprehensive evaluation criteriatechnology

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