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A Convolutional Neural Network-Based Maximum Power Point Voltage Forecasting Method for Pavement PV Array
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DOI：10.1109/TIM.2022.3227552.png)
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Abstract
En 中文
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.
Keywords:
Prediction algorithms
Convolutional neural networks
Machine learning algorithms
Forecasting
Roads
Neural networks
Classification algorithms
Convolutional neural network (CNN)
feature extraction
maximum power point (MPP) voltage forecasting model
pavement PV array
vehicle shadow image
Journal
IF:
5.9
Papers:
2.0W
Citations:
5.8W
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