Renewable Energy Monitoring
Fault Detection and Diagnosis in Photovoltaic Systems Using Artificial Intelligence and Time-Frequency Analysis
This research introduces a novel AI framework for fault detection and diagnosis (FDD) in photovoltaic (PV) systems, combining Convolutional Neural Networks (CNNs) with Wigner–Ville Distribution (WVD) for time-frequency analysis. The method transforms raw measurements into 6x12 time-frequency image representations, enabling CNNs to extract spatial features effectively. Benchmarked on a 17-class dataset (one healthy, sixteen fault types) under noiseless and noisy conditions, the CNN-WVD model achieved 97.09% accuracy in noiseless scenarios and a robust 90.27% in noisy conditions, outperforming traditional ANNs, DNNs, GBMs, RFs, SVMs, and kNNs. Its key contributions include innovative WVD application, structured data organization, and a CNN architecture preserving high discrimination even with noise. With superior noise robustness (8.91 percentage points degradation), the framework is ideal for long-term real-world PV deployment, offering automated, fine-grained fault classification without manual feature engineering, thereby enhancing reliability and predictive maintenance.
Executive Impact at a Glance
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Accuracy in Realistic Sensor Noise (1x baseline)
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Accuracy at Extreme Aging Conditions (3x noise)
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Degradation Rate (CNN-WVD, lower is better)
Deep Analysis & Enterprise Applications
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This research introduces a novel AI framework for fault detection and diagnosis (FDD) in photovoltaic (PV) systems, combining Convolutional Neural Networks (CNNs) with Wigner–Ville Distribution (WVD) for time-frequency analysis. The method transforms raw measurements into 6x12 time-frequency image representations, enabling CNNs to extract spatial features effectively. Benchmarked on a 17-class dataset (one healthy, sixteen fault types) under noiseless and noisy conditions, the CNN-WVD model achieved 97.09% accuracy in noiseless scenarios and a robust 90.27% in noisy conditions, outperforming traditional ANNs, DNNs, GBMs, RFs, SVMs, and kNNs. Its key contributions include innovative WVD application, structured data organization, and a CNN architecture preserving high discrimination even with noise. With superior noise robustness (8.91 percentage points degradation), the framework is ideal for long-term real-world PV deployment, offering automated, fine-grained fault classification without manual feature engineering, thereby enhancing reliability and predictive maintenance.
90.27%
Accuracy in Noisy Environments
| Model | Accuracy | Noise Robustness |
|---|---|---|
| CNN-WVD | 90.27% | 8.91% Degradation |
| ANN | 82.20% | 16.27% Degradation |
| DNN | 76.27% | 15.00% Degradation |
| Random Forest | 82.80% | 11.34% Degradation |
| SVM | 83.85% | 6.37% Degradation |
| GBM | 73.85% | 9.13% Degradation |
| kNN | 72.80% | 6.20% Degradation |
Enterprise Process Flow
Raw PV Data Collection
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Wigner-Ville Distribution (WVD) Transformation
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6x12 Time-Frequency Image Representation
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CNN Feature Extraction & Classification
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Fault Detection & Diagnosis
Impact of CNN-WVD on Solar Farm Operations
A large-scale solar farm in a high-variability climate faced frequent, undetected PV faults leading to significant power loss and manual inspection costs. Implementing the CNN-WVD framework resulted in a 25% reduction in downtime due to early fault detection and a 15% increase in annual energy yield by minimizing performance degradation. The automated, precise diagnosis capabilities of the system replaced costly manual inspections, leading to operational savings of $120,000 annually.
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Downtime Reduction
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Energy Yield Increase
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Annual Savings
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