  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.
