Advanced Fault Diagnosis in Photovoltaic Arrays Using a Metropolis-Hastings Convolutional Variational Autoencoder
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10258987" target="_blank" >RIV/61989100:27730/25:10258987 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11132305" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11132305</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3601530" target="_blank" >10.1109/ACCESS.2025.3601530</a>
Alternative languages
Result language
angličtina
Original language name
Advanced Fault Diagnosis in Photovoltaic Arrays Using a Metropolis-Hastings Convolutional Variational Autoencoder
Original language description
Effective fault diagnosis is critical for maintaining the operational efficiency and reliability of photovoltaic (PV) arrays. While a multitude of artificial intelligence techniques have been applied to detect and diagnose faults in solar panel systems, this research presents a novel fault diagnosis strategy. Our approach leverages a Variational Autoencoder (VAE), further enhanced by Metropolis-Hastings Monte Carlo sampling and a robust residual convolutional neural network architecture. The proposed Metropolis-Hastings Convolutional Variational Autoencoder (MH-CVAE) demonstrates precise identification and categorization of diverse PV faults-including arc faults, MPPT failures, line-to-line, open circuits, degradation, and partial shading-under varied operational influences. This methodology is benchmarked against established machine learning techniques and existing autoencoder models reported in the literature. Comprehensive simulations highlight the MH-CVAE model's superior performance, attaining a remarkable 99.86% accuracy on simulated test data and surpassing conventional diagnostic approaches. This novel approach significantly expands diagnostic capabilities for PV arrays, thereby offering potential improvements in system dependability and overall operational efficacy. Consequently, the MH-CVAE method shows considerable promise for advancing PV array fault diagnosis, ultimately contributing to the development of more resilient and efficient solar energy infrastructures.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
—
Continuities
O - Projekt operacniho programu
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
—
Volume of the periodical
13
Issue of the periodical within the volume
Valume 13
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
150537-150554
UT code for WoS article
001562578800029
EID of the result in the Scopus database
—