Abstract
The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Firstly, we combine the asymmetric absolute value function from the quantile regression (QR) cost function with logistic functions (LF), so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles without considering the prior distribution of wind power. Among them, three optimization algorithms are selected to make comparative studies. Secondly, an adaptive outlier filtering method is developed based on QRLF, which can eliminate the outliers by the symmetrical relationship of power distribution. Lastly, supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling.
Highlights
The wind turbine power curve (WTPC) is defined as the relationship between electrical power output and hub height wind speed of a wind turbine [1], and it is important for energy assessment, wind power forecasting and condition monitoring [2]
The results show that 5P-QRLF optimized by Particle swarm optimization (PSO) generally has the best fitting performance
Compared with DBSCAN-Filter, the filtering results of the proposed QRLF-Filter are more robust and easy to deploy in actual wind farms
Summary
The wind turbine power curve (WTPC) is defined as the relationship between electrical power output and hub height wind speed of a wind turbine [1], and it is important for energy assessment, wind power forecasting and condition monitoring [2]. According to [19,20,21,22,23], the Gaussian process (GP) is the widely used method for probabilistic WTPC modelling, which can qualify the uncertainty of power generation via the predicted confidence intervals (CI). This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Considering the structure of LF and QR, we combine the asymmetric absolute value function from the QR cost function [29] with the LF model parameters, so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles. We propose a novel outlier filtering method that utilizes the symmetrical relationship of power distribution.
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