Photovoltaic Array Fault Diagnosis Using a semi-supervised method based on Generative Adversarial Networks (GANs)

Document Type : Original Research

Authors

1 Department of Electrical Engineering, Ur.C., Islamic Azad University, Urmia, Iran.

2 Department of Electrical Engineering, Ur.C., Islamic Azad University, Urmia, Iran

Abstract
Reliable fault diagnosis in photovoltaic (PV) arrays is essential for reducing energy losses, preventing safety hazards, and maintaining system availability; however, conventional supervised learning methods require large labeled datasets that are costly and risky to obtain from operating plants. This paper proposes a data-efficient diagnostic framework that combines convolutional neural networks, semi-supervised generative adversarial learning, and few-shot meta-learning. A 5.3-kW, 6 × 3 PV array is modeled in MATLAB/Simulink to generate healthy and faulty operating data under variations in irradiance, temperature, fault resistance, and partial shading. Four conditions are considered: normal operation, line-to-line faults, hot-spot or high-series-resistance faults, and open-circuit faults. Current, voltage, irradiance, and temperature signals are normalized and transformed using the short-time Fourier transform; their spectrograms are then concatenated into composite images for automatic feature extraction and classification. A three-layer CNN provides a high-accuracy supervised benchmark, reaching approximately 99.5% accuracy on noise-free data. To reduce dependence on labeled samples, a semi-supervised GAN exploits unlabeled and synthetically generated data and achieves 81.57% test accuracy using only 100 labeled samples on the one-dimensional noisy dataset. To further address the computational cost of GAN training and extreme label scarcity, a few-shot meta-learning model is introduced. In the 5-shot, 4-way setting, it achieves 94.03% accuracy on previously unseen samples, substantially outperforming a conventional CNN trained with limited labeled data. The main contribution is therefore a unified comparison of three complementary learning strategies and a practical few-shot solution that preserves strong diagnostic performance with minimal labeled PV fault data under realistic operating variability.

Keywords

Subjects

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Volume 3, Issue 4
Autumn 2026
Pages 26-41

  • Receive Date 13 May 2026
  • Revise Date 17 August 2026
  • Accept Date 10 September 2026
  • First Publish Date 10 September 2026
  • Publish Date 01 December 2026