A Convolutional Neural Network Based Maximum Power Point Voltage Forecasting Method for Pavement PV Array
Permanent link to this recordhttp://hdl.handle.net/10754/686363
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AbstractThe 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 the 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 paper proposes a maximum power point 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 maximum power point 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 maximum power point voltage of pavement PV array under different vehicle shading conditions.
CitationMao, M., Feng, X., Xin, J., & Chow, T. W. S. (2022). A Convolutional Neural Network Based Maximum Power Point Voltage Forecasting Method for Pavement PV Array. IEEE Transactions on Instrumentation and Measurement, 1–1. https://doi.org/10.1109/tim.2022.3227552
SponsorsThis work was supported in part by the National Natural Science Foundation of China under Grant 52107177 and Grant 62073272, in part by the International Postdoctoral Exchange Fellowship Program under Grant 2020045.