Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
CMC-Computers Materials & Continua
ISSN
1546-2218
e-ISSN
1546-2226
Volume of the periodical
72
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
3999-4017
UT code for WoS article
000779567700035
EID of the result in the Scopus database
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