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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

9 Scopus Citations
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Original languageEnglish
Pages (from-to)1111-1124
Journal / PublicationIEEE Transactions on Power Systems
Issue number2
Online published5 May 2022
Publication statusPublished - Mar 2023


This study develops a novel data-synthesis-informedtraining 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.

Research Area(s)

  • Adaptation models, Data models, data-driven model ACRONYMS 4PLF Four-parameter logistic function 5PLF Five-parameter logistic function ADE Adjusted double exponential BSA Backtracking search algorithm CDF Cumulative distribution function CWS Cut-in wind speed DCAE Deep convolutional autoencoder DE Double exponential DIT Data-synthesis-informed-training DITDCAE Data-synthesis-informed-training deep convolutional autoencoder DITU-net Data-synthesis-informed-training U-net DKC Domain knowledge correction IDP Insufficient data pattern KNN K nearest neighbor MAE Mean absolute error MAPE Mean absolute percentage error ML Machine learning MS Marker size, Machine vision, Mathematical models, neural networks, Training, wind energy, wind power curve, Wind power generation, Wind speed, wind turbine, Data-driven model

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