A Convolutional Neural Network-Based Maximum Power Point Voltage Forecasting Method for Pavement PV Array

Mingxuan Mao*, Xinying Feng, Jihao Xin, Tommy W.S. Chow

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The shadows formed by fast-moving vehicles on a pavement PV array exhibit complex dynamic random distribution characteristics, which can cause a dynamic multipeak PV curve. Dynamic vehicle shadow will cause a reduction in pavement PV power, so the question is how to maximize the power in such conditions by operating at different maximum power point (MPP) quickly and continually. To address this issue, this article proposes an MPP voltage forecasting method based on convolutional neural network (CNN). This method inputs the environmental information of pavement PV array into the proposed CNN model for learning and then uses this model to forecast the MPP voltage. Finally, simulation and experimental test with ResNet, MLP, and CNN methods are carried out and the comparison results show that this model can accurately predict the MPP voltage of pavement PV array under different vehicle shading conditions.

Original languageEnglish (US)
Article number2503109
JournalIEEE Transactions on Instrumentation and Measurement
Volume72
DOIs
StatePublished - 2023

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 52107177 and Grant 62073272 and in part by the International Postdoctoral Exchange Fellowship Program under Grant 2020045.

Publisher Copyright:
© 1963-2012 IEEE.

Keywords

  • Convolutional neural network (CNN)
  • feature extraction
  • maximum power point (MPP) voltage forecasting model
  • pavement PV array
  • vehicle shadow image

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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