LAPSE:2019.0193
Published Article
LAPSE:2019.0193
A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data
January 31, 2019
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.
Record ID
Keywords
prediction model, short-term outage model, supervisory control and data acquisition (SCADA) data, wind turbine (WT)
Subject
Suggested Citation
Sun P, Li J, Chen J, Lei X. A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data. (2019). LAPSE:2019.0193
Author Affiliations
Sun P: The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China
Li J: The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China
Chen J: The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China
Lei X: The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China
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Li J: The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China
Chen J: The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China
Lei X: The State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400044, China
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Journal Name
Energies
Volume
9
Issue
11
Article Number
E882
Year
2016
Publication Date
2016-10-28
Published Version
ISSN
1996-1073
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Original Submission
Other Meta
PII: en9110882, Publication Type: Journal Article
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Published Article
LAPSE:2019.0193
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External Link
doi:10.3390/en9110882
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Version History
[v1] (Original Submission)
Jan 31, 2019
Verified by curator on
Jan 31, 2019
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v1
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https://psecommunity.org/LAPSE:2019.0193
Original Submitter
Calvin Tsay
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