Generative Wind Power Curve Modeling Via Machine Vision: A Self-learning Deep Convolutional Network Based Method

TL;DR

This paper introduces a self-learning deep convolutional network approach, based on U-net, for wind power curve modeling using machine vision, eliminating the need for data pre-processing and demonstrating superior performance over classical methods.

Contribution

The paper presents a novel self-training U-net method that models wind power curves as images, enabling automated, pre-processing-free modeling from synthesized and observed data.

Findings

Outperforms classical WPC modeling methods in experiments

Successfully generates wind power curves from observed SCADA data

Requires only a single training phase without data pre-processing

Abstract

This paper develops a novel self-training U-net (STU-net) based method for the automated WPC model generation without requiring data pre-processing. The self-training (ST) process of STU-net has two steps. First, different from traditional studies regarding the WPC modeling as a curve fitting problem, in this paper, 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) data based on a set of S-shape functions depicting WPCs. These synthesized SCADA data and WPC functions are visualized as images and paired as training samples(I_x, I_wpc). A U-net is then developed to approximate the model recovering I_wpc from I_x. The developed U-net is applied into observed SCADA data and can successfully generate the I_wpc. Moreover, we develop a pixel mapping and correction process to derive a mathematical form f_wpc representing I_wpcgenerated previously. The proposed STU-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 by benchmarking against classical WPC modeling methods. To demonstrate the repeatability of the presented research, we release our code at https://github.com/IkeYang/STU-net.