A novel data-driven deep learning approach for wind turbine power curve modeling

Abstract

Existing wind turbine power curve (WTPC) models have limited performance in capturing the complex relationship between wind speed and wind power due to their inadequate nonlinear fitting abilities. Deep learning (DL) excels at describing complex relationships. However, it is typically not applicable to WTPC modeling with a single wind speed input. This study proposes a novel data-driven DL approach mELM-CA-CNN to establish WTPCs based on multiple extreme learning machines (ELMs), channel attention (CA), convolutional neural network (CNN), and Huber loss (HL). First, multiple ELMs map a single wind speed to various high-dimensional feature spaces. Then, CA helps reduce redundant mappings of ELMs. Next, CNN extracts important features from all ELM mappings and models the complex relationship between wind speed and the corresponding power. Finally, the proposed model is trained with the differentiable and robust HL. To reduce the adverse impact of outliers on WTPC modeling, a segmented data cleaning approach based on 3 σ criterion and quartile algorithm is proposed. Comparisons with ten popular WTPC models demonstrate that mELM-CA-CNN obtains the most accurate WTPCs on four wind datasets, showing the superiority of the proposed DL approach. Moreover, the roles of the different modules of mELM-CA-CNN in improving model performance are verified.