DOAJ Open Access 2024

A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems

Shahabodin Afrasiabi Sarah Allahmoradi Mousa Afrasiabi Xiaodong Liang C. Y. Chung +1 lainnya

Abstrak

In this paper, a robust, multi-modal deep-learning-based fault identification method is proposed for solar photovoltaic (PV) systems, capable of detecting a wide range of faults at PV arrays, inverters, sensors, and grid connections. The proposed method combines residual convolutional neural networks (CNNs) and gated recurrent units (GRUs) to effectively extract both spatial and temporal features from raw PV data. To enhance the proposed model’s robustness and accuracy, a probabilistic loss function based on the entropy theory is formulated. The proposed method is validated using both experimental data obtained from a PV emulator-based test system and simulation data, achieving over 98% accuracy in fault identification under various noise conditions. The results indicate that the proposed model outperforms conventional CNN- and MSVM-based methods, demonstrating its potential in providing precise fault diagnostics in PV systems.

Topik & Kata Kunci

Distribution or transmission of electric power Production of electric energy or power. Powerplants. Central stations

Penulis (6)

Shahabodin Afrasiabi

Sarah Allahmoradi

Mousa Afrasiabi

Xiaodong Liang

C. Y. Chung

Jamshid Aghaei

Format Sitasi

APA MLA BibTeX

Afrasiabi, S., Allahmoradi, S., Afrasiabi, M., Liang, X., Chung, C.Y., Aghaei, J. (2024). A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems. https://doi.org/10.1109/OAJPE.2024.3497880

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Informasi Jurnal

Tahun Terbit: 2024
Sumber Database: DOAJ
DOI: 10.1109/OAJPE.2024.3497880
Akses: Open Access ✓