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.
Highlights
In consideration of the intermittency and randomness of wind power, the large-scale wind power integration has a great influence on the safe and stable operation of the electrical power system [1,2]
The electric power system dispatching is mainly based on the short-term wind speed and wind power prediction [4,5,6], which fails to consider the impact of wind turbine (WT) outages on the wind power variation
This paper presents a novel approach for short‐term WT outage probability prediction
Summary
In consideration of the intermittency and randomness of wind power, the large-scale wind power integration has a great influence on the safe and stable operation of the electrical power system [1,2]. Compared with the traditional electric transmission and transformation equipment, the WTs have abundant condition monitoring data, which could provide comprehensive condition information for the WT short-term operational reliability evaluation. The short-term change in the SCADA monitoring parameters are closely related to the external environment and operation condition of WTs. By using advanced SCADA data mining methods, various condition parameter prediction models have been developed to detect the significant changes in WT behavior prior to fault occurrences [15,16,17,18,19]. This paper presents a short-term (i.e., 15-min-ahead) WT outage model based on condition parameters obtained from the wind farm SCADA system.
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