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 language||English (US)|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|State||Published - 2023|
Bibliographical noteFunding 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.
© 1963-2012 IEEE.
- Convolutional neural network (CNN)
- feature extraction
- maximum power point (MPP) voltage forecasting model
- pavement PV array
- vehicle shadow image
ASJC Scopus subject areas
- Electrical and Electronic Engineering