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<p>Converting energy from solar panels to electric power includes several advantages; it’s considered clean, green, and available. These points make the system a good, attractive, and reliable alternative to electrical power production. Several factors influence the solar system performance, leading to production power loss. Among these are mismatches resulting from partial shading and electrical breakdowns within the PV array, such as open circuits and short circuits. Therefore, it’s necessary to carefully study these faults to address them quickly and ensure the system continues operating without interruption. This study proposes a new hybrid deep learning model organized into three stages. The proposed system’s strength lies in the introduced techniques based on Bayesian probabilistic reasoning. Stage 1 is a compression stage. It consists of two Variational Auto-encoders (VAE) with unsupervised learning: the first VAE receives the current, voltage, and power signals post-processed by the Discrete Wavelet Transform (DWT) and returns 1000 descriptors instead of 4692, while the second VAE receives the first VAE outputs and returns 200 descriptors. Stage 2 consists of a Cramer-Singer Multi-Class Support Vector Machine (CS-MSVM) type classifier. It gets the second VAE output and returns the liking score for three default categories plus the regular class. Stage 3 is a probabilistic decision-level stage; it post-processes the CS-MSVM outputs to produce probability outputs instead of an all-or-nothing decision. The present system was evaluated and validated using statistical metrics, demonstrating its superiority and effectiveness compared to other intelligent techniques also tested in this work. Additionally, noisy data were incorporated to study environmental phenomena under realistic conditions.</p>

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A hybrid deep learning model based on variational auto encoder and probabilistic CS-MSVM for fault detection in PV panels

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Sakina Behilil,
  • Mounia Hendel,
  • Khadra Kessairi,
  • Imen Souhila Bousmaha,
  • Mostefa Brahami

摘要

Converting energy from solar panels to electric power includes several advantages; it’s considered clean, green, and available. These points make the system a good, attractive, and reliable alternative to electrical power production. Several factors influence the solar system performance, leading to production power loss. Among these are mismatches resulting from partial shading and electrical breakdowns within the PV array, such as open circuits and short circuits. Therefore, it’s necessary to carefully study these faults to address them quickly and ensure the system continues operating without interruption. This study proposes a new hybrid deep learning model organized into three stages. The proposed system’s strength lies in the introduced techniques based on Bayesian probabilistic reasoning. Stage 1 is a compression stage. It consists of two Variational Auto-encoders (VAE) with unsupervised learning: the first VAE receives the current, voltage, and power signals post-processed by the Discrete Wavelet Transform (DWT) and returns 1000 descriptors instead of 4692, while the second VAE receives the first VAE outputs and returns 200 descriptors. Stage 2 consists of a Cramer-Singer Multi-Class Support Vector Machine (CS-MSVM) type classifier. It gets the second VAE output and returns the liking score for three default categories plus the regular class. Stage 3 is a probabilistic decision-level stage; it post-processes the CS-MSVM outputs to produce probability outputs instead of an all-or-nothing decision. The present system was evaluated and validated using statistical metrics, demonstrating its superiority and effectiveness compared to other intelligent techniques also tested in this work. Additionally, noisy data were incorporated to study environmental phenomena under realistic conditions.