Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21720%2F24%3A00373638" target="_blank" >RIV/68407700:21720/24:00373638 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/su16031012" target="_blank" >https://doi.org/10.3390/su16031012</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/su16031012" target="_blank" >10.3390/su16031012</a>

Result language

angličtina

Original language name

Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)

Original language description

The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20704 - Energy and fuels

Project

<a href="/en/project/TK70020002" target="_blank" >TK70020002: DEPLOYMENT OF SMART RENEWABLE ENERGY COMMUNITIES</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

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

Name of the periodical

Sustainability — Open Access Journal

ISSN

2071-1050

e-ISSN

2071-1050

Volume of the periodical

16

Issue of the periodical within the volume

3

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

1-21

UT code for WoS article

001160385000001

EID of the result in the Scopus database

2-s2.0-85185313287