Photovoltaic Module Fault Detection Based on a Convolutional Neural Network
Abstract
:1. Introduction
2. Architecture of the System
2.1. Photovoltaic Module Fault Signal Capture
2.2. PV Module Defect Construction
2.2.1. Normal PV Module (Type 1)
2.2.2. PV Module Breakage (Type 2)
2.2.3. PV Module Contact Defectiveness (Type 3)
2.2.4. PV Module Diode Failure (Type 4)
3. Proposed Fault Diagnosis Algorithm
3.1. Chaos Synchronization Detection Method
3.2. Convolutional Neural Networks
3.2.1. Convolution Layer
3.2.2. Pooling Layer
3.2.3. Fully-Connected Layer
3.2.4. Activation Layer
4. Results
4.1. Original Signal Captured
4.2. Convolutional Neural Network Recognition Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Work Style | PV Module Defect Construction |
---|---|
(Off-line) | normal PV module (Type1) |
PV module breakage (Type2) | |
PV module contact defectiveness (Type3) | |
PV module bypass diode failure (Type4) |
Algorithm | Epoch | Training Rate (%) | Accuracy Rate (%) | Training Time (s) | Ranking |
---|---|---|---|---|---|
CNN + Lorenz | 50 | 100 | 99.5 | 9 | 1 |
CNN + Lorenz | 50 | 100 | 98 | 9 | 2 |
ENN + Lorenz | 100 | 97.2 | 86.75 | 0.154 | 3 |
ENN + Lorenz | 100 | 95.5 | 83.25 | 0.146 | 4 |
ENN + Lorenz | 100 | 95.25 | 80.25 | 0.143 | 5 |
CNN + Lorenz | 50 | 100 | 74 | 9 | 6 |
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Lu, S.-D.; Wang, M.-H.; Wei, S.-E.; Liu, H.-D.; Wu, C.-C. Photovoltaic Module Fault Detection Based on a Convolutional Neural Network. Processes 2021, 9, 1635. https://doi.org/10.3390/pr9091635
Lu S-D, Wang M-H, Wei S-E, Liu H-D, Wu C-C. Photovoltaic Module Fault Detection Based on a Convolutional Neural Network. Processes. 2021; 9(9):1635. https://doi.org/10.3390/pr9091635
Chicago/Turabian StyleLu, Shiue-Der, Meng-Hui Wang, Shao-En Wei, Hwa-Dong Liu, and Chia-Chun Wu. 2021. "Photovoltaic Module Fault Detection Based on a Convolutional Neural Network" Processes 9, no. 9: 1635. https://doi.org/10.3390/pr9091635