Generative Wind Power Curve Modeling via Machine Vision: A Deep Convolutional Network Method With Data-Synthesis-Informed-Training

Abstract

This study develops a novel data-synthesis-informed-training U-net (DITU-net) based method to automate the wind power curve (WPC) modeling without data pre-processing. The proposed data-synthesis-informed-training (DIT) process has following steps. First, different from traditional studies regarding the WPC modeling as a curve fitting problem, we renovate the WPC modeling formulation from a machine vision aspect. To develop sufficiently diversified training samples, we synthesize supervisory control and data acquisition (SCADA) WPC data based on a set of S-shape functions representing WPCs. These synthesized SCADA data and WPC functions are visualized as images, named the synthesized SCADA WPC and synthesized neat WPC, and paired as training samples. A deep generative model based on U-net is developed to approximate the projection recovering the synthesized neat WPC from the synthesized SCADA WPC. The developed U-net based model is applied into observed SCADA data and can successfully generate the neat WPC. Moreover, a pixel mapping and correction process is developed to derive a mathematical form depicting the neat WPC generated previously. The proposed DITU-net only needs to train once and does not require any data preprocessing in applications. Numerical experiments based on 76 WTs are conducted to validate the superiority of the proposed method via benchmarking against classical WPC modeling methods.