A versatile way for outlier identification in two-dimensional space is proposed.
Periodically updated deterministic and uncertainty WTPC modeling are presented.
A daily multi-index system is provided to capture different features of WTPC.
Daily comprehensive rating system of wind turbine power generation is realized.
With development of grid-connected wind power, data-driven wind turbine power curve (WTPC) becomes vital for many valuable applications whereas it is hugely affected by the potential outliers in supervisory control and data acquisition (SCADA) system. In this paper, high-fidelity WTPC modeling and its application on condition monitoring are deeply studied. Firstly, through irregular space-division and nonlinear space-mapping, stepwise data cleaning procedure is proposed. On this basis, the cleaned data are used for high-fidelity modeling where optimized least square support vector machine (LSSVM) is chosen for deterministic WTPC modeling and conditional kernel density estimation (CKDE) is selected for uncertainty modeling after comparison where the models are updated via the sliding time window mechanism. Using the consistency of historical data-driven models in a certain future time window, a daily condition monitoring system, including daily multi-index system and improved fuzzy comprehensive evaluation method (FCEM), is established to monitor future wind turbine condition. Finally, measured data from a wind farm in north China are acquired for validation. The results show that the outliers are effectively cleaned for high-fidelity WTPC modeling. The daily condition monitoring system including daily raw data preprocessing, multi-index system and FCEM algorithm can provide efficient daily rating score.
Wind turbine power curve
Daily condition monitoring
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