<p>This research introduces a novel artificial intelligence (AI) framework for fault detection and diagnosis (FDD) in photovoltaic (PV) systems that combines Convolutional Neural Networks (CNNs) with time–frequency analysis via the Wigner–Ville Distribution (WVD). The proposed method transforms raw numerical measurements—including solar irradiance, temperature, voltage, current, and power—into compact 6 × 12 time–frequency image representations, enabling effective spatial feature extraction by CNNs that are well suited to image-like data. The framework is benchmarked under both noiseless and noisy conditions on a comprehensive 17‑class dataset comprising one healthy condition (C0) and sixteen fault types (F1–F16), including progressive short‑circuit faults within a single string, pure partial‑shading faults, and combined inter‑string short‑circuit and asymmetric partial‑shading patterns along PV strings. To contextualize performance, the CNN–WVD model is compared not only with classical Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) but also with Gradient Boosting Machines (GBM), Random Forests (RF), Support Vector Machines (SVM), and k‑Nearest Neighbors (kNN), all trained on the same WVD‑transformed data. In noiseless conditions, ANN and DNN achieve 99.51% and 99.49% accuracy, respectively, while the CNN attains 97.09%; RF, SVM, GBM, and kNN reach 93.47%, 88.62%, 84.01%, and 75.69% accuracy. Under noisy conditions that emulate real PV environments, the CNN is the most robust model with 90.27% accuracy, outperforming ANN (82.20%), RF (82.80%), SVM (83.85%), GBM (73.85%), DNN (76.27%), and kNN (72.80%). Key contributions include: (i) the use of WVD to obtain highly informative time–frequency representations of PV electrical signals, (ii) a structured data‑organization strategy that maps multivariate PV measurements into fixed‑size WVD images, and (iii) a CNN architecture that preserves high discrimination capability across closely related fault severities and locations, even in the presence of noise achieving 90.8% accuracy under realistic sensor noise (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(1 \times\)</EquationSource> </InlineEquation> baseline uncertainty: <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\pm 10{W \mathord{\left/ {\vphantom {W {m^{2} }}} \right. \kern-0pt} {m^{2} }}\)</EquationSource> </InlineEquation> irradiance, <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\pm 2 \, C\)</EquationSource> </InlineEquation> temperature, <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\pm 5 \, V\)</EquationSource> </InlineEquation> voltage, <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\pm 1 \, A\)</EquationSource> </InlineEquation> current, <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\pm 25 \, W\)</EquationSource> </InlineEquation> power) and maintaining 71.5% accuracy at <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(3 \times\)</EquationSource> </InlineEquation> noise, representing extreme aging sensor conditions. With a competitive degradation of only 8.91 percentage points—lower than the neural-network baselines (ANN: 16.27%, DNN: 15.00%) and the tree ensemble RF (11.34%)—the CNN + WVD framework demonstrates superior noise robustness for long-term deployment in real-world PV installations. By bridging advanced time–frequency analysis with deep learning and systematically comparing against a broad set of machine‑learning baselines, the proposed framework enables fully automated, fine‑grained PV fault classification without manual feature engineering, thereby enhancing monitoring reliability, reducing downtime, and supporting predictive maintenance in large‑scale PV deployments.</p>

Fault detection and diagnosis in photovoltaic systems using artificial intelligence and time–frequency analysis

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2026年2月20日

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Abdellatif Seghiour,
Yacine Bendjeddou,
Imene Meriem Mostefaoui,
Aissa Chouder,
Hisham Alharbi,
Abdullah S. Bin Humayd,
Abebe Wondiferaw Wondimeneh,
Abdulrahman Babqi

摘要

This research introduces a novel artificial intelligence (AI) framework for fault detection and diagnosis (FDD) in photovoltaic (PV) systems that combines Convolutional Neural Networks (CNNs) with time–frequency analysis via the Wigner–Ville Distribution (WVD). The proposed method transforms raw numerical measurements—including solar irradiance, temperature, voltage, current, and power—into compact 6 × 12 time–frequency image representations, enabling effective spatial feature extraction by CNNs that are well suited to image-like data. The framework is benchmarked under both noiseless and noisy conditions on a comprehensive 17‑class dataset comprising one healthy condition (C0) and sixteen fault types (F1–F16), including progressive short‑circuit faults within a single string, pure partial‑shading faults, and combined inter‑string short‑circuit and asymmetric partial‑shading patterns along PV strings. To contextualize performance, the CNN–WVD model is compared not only with classical Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) but also with Gradient Boosting Machines (GBM), Random Forests (RF), Support Vector Machines (SVM), and k‑Nearest Neighbors (kNN), all trained on the same WVD‑transformed data. In noiseless conditions, ANN and DNN achieve 99.51% and 99.49% accuracy, respectively, while the CNN attains 97.09%; RF, SVM, GBM, and kNN reach 93.47%, 88.62%, 84.01%, and 75.69% accuracy. Under noisy conditions that emulate real PV environments, the CNN is the most robust model with 90.27% accuracy, outperforming ANN (82.20%), RF (82.80%), SVM (83.85%), GBM (73.85%), DNN (76.27%), and kNN (72.80%). Key contributions include: (i) the use of WVD to obtain highly informative time–frequency representations of PV electrical signals, (ii) a structured data‑organization strategy that maps multivariate PV measurements into fixed‑size WVD images, and (iii) a CNN architecture that preserves high discrimination capability across closely related fault severities and locations, even in the presence of noise achieving 90.8% accuracy under realistic sensor noise ( \(1 \times\) baseline uncertainty: \(\pm 10{W \mathord{\left/ {\vphantom {W {m^{2} }}} \right. \kern-0pt} {m^{2} }}\) irradiance, \(\pm 2 \, C\) temperature, \(\pm 5 \, V\) voltage, \(\pm 1 \, A\) current, \(\pm 25 \, W\) power) and maintaining 71.5% accuracy at \(3 \times\) noise, representing extreme aging sensor conditions. With a competitive degradation of only 8.91 percentage points—lower than the neural-network baselines (ANN: 16.27%, DNN: 15.00%) and the tree ensemble RF (11.34%)—the CNN + WVD framework demonstrates superior noise robustness for long-term deployment in real-world PV installations. By bridging advanced time–frequency analysis with deep learning and systematically comparing against a broad set of machine‑learning baselines, the proposed framework enables fully automated, fine‑grained PV fault classification without manual feature engineering, thereby enhancing monitoring reliability, reducing downtime, and supporting predictive maintenance in large‑scale PV deployments.