Enhanced Fault Detection and Diagnosis in Photovoltaic Arrays Using a Hybrid NCA-CNN Model

TURHAL Ü. Ç., ÖNAL Y., TURHAL K.

CMES - Computer Modeling in Engineering and Sciences, cilt.143, sa.2, ss.2307-2332, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 143 Sayı: 2
  • Basım Tarihi: 2025
  • Doi Numarası: 10.32604/cmes.2025.064269
  • Dergi Adı: CMES - Computer Modeling in Engineering and Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2307-2332
  • Anahtar Kelimeler: Artificial intelligence, convolutional neural networks (CNN), machine learning, neighbourhood component analysis (NCA), photovoltaic energy systems, photovoltaic fault detection and diagnosis
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

The reliability and efficiency of photovoltaic (PV) systems are essential for sustainable energy production, requiring accurate fault detection to minimize energy losses. This study proposes a hybrid model integrating Neighborhood Components Analysis (NCA) with a Convolutional Neural Network (CNN) to improve fault detection and diagnosis. Unlike Principal Component Analysis (PCA), which may compromise class relationships during feature extraction, NCA preserves these relationships, enhancing classification performance. The hybrid model combines NCA with CNN, a fundamental deep learning architecture, to enhance fault detection and diagnosis capabilities. The performance of the proposed NCA-CNN model was evaluated against other models. The experimental evaluation demonstrates that the NCA-CNN model outperforms existing methods, achieving 100% fault detection accuracy and 99% fault diagnosis accuracy. These findings underscore the model’s potential in improving PV system reliability and efficiency.