Advanced Fault Diagnosis in Photovoltaic Arrays Using a Metropolis-Hastings Convolutional Variational Autoencoder

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Advanced Fault Diagnosis in Photovoltaic Arrays Using a Metropolis-Hastings Convolutional Variational Autoencoder

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

Advanced Fault Diagnosis in Photovoltaic Arrays Using a Metropolis-Hastings Convolutional Variational Autoencoder

Popis výsledku anglicky

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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

Valume 13

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

150537-150554

Kód UT WoS článku

001562578800029

EID výsledku v databázi Scopus

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