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 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.
| Original language | English |
|---|---|
| Article number | 2503109 |
| Journal | IEEE Transactions on Instrumentation and Measurement |
| Volume | 72 |
| Online published | 8 Dec 2022 |
| DOIs | |
| Publication status | Published - 2023 |
Research Keywords
- Classification algorithms
- convolutional neural network (CNN)
- Convolutional neural networks
- feature extraction
- Forecasting
- Machine learning algorithms
- maximum power point voltage forecasting model
- Neural networks
- Pavement PV array
- Prediction algorithms
- Roads
- vehicle shadow image
