Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144351" target="_blank" >RIV/00216305:26220/22:PU144351 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.techscience.com/cmc/v72n2/47234" target="_blank" >https://www.techscience.com/cmc/v72n2/47234</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2022.026353" target="_blank" >10.32604/cmc.2022.026353</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

Popis výsledku v původním jazyce

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic (PV) system and highlighted along with a brief overview on existing fault detection methodology. Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement. Further, the system was tested with a 4 kWp grid-connected PV system, and a decision tree-based algorithm was developed for the identification of a fault. The results identified 94.7% training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

Název v anglickém jazyce

Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

Popis výsledku anglicky

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic (PV) system and highlighted along with a brief overview on existing fault detection methodology. Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement. Further, the system was tested with a 4 kWp grid-connected PV system, and a decision tree-based algorithm was developed for the identification of a fault. The results identified 94.7% training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

CMC-Computers Materials & Continua

ISSN

1546-2218

e-ISSN

1546-2226

Svazek periodika

72

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

3999-4017

Kód UT WoS článku

000779567700035

EID výsledku v databázi Scopus

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