The objective of using a Deep Neural Network (DNN) for Photovoltaic (PV) Maximum Power Point Tracking (MPPT) is to improve the efficiency and accuracy of tracking the maximum power point of a solar panel system. The maximum power point (MPP) is the operating point at which the solar panel generates the highest possible output power for a given set of environmental conditions (such as sunlight intensity and temperature).
Benefits of using a DNN-based PV MPPT system include:
- Adaptability: DNNs can capture intricate patterns and adapt to varying environmental conditions, potentially leading to improved MPPT accuracy.
- Complex Relationships: DNNs can model complex and nonlinear relationships that might be challenging for traditional methods.
- Flexibility: The model can be fine-tuned and updated as new data becomes available, improving performance over time.
- Efficiency: Once trained, the DNN can perform MPPT calculations more efficiently compared to iterative methods.
for more information.
引用格式
PIRC (2024). Deep Neural Network for PV MPPT (https://www.mathworks.com/matlabcentral/fileexchange/133667-deep-neural-network-for-pv-mppt), MATLAB Central File Exchange. 检索来源 .
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