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dc.contributor.authorJanarthanan, Ramadoss
dc.contributor.authorMaheshwari, R Uma
dc.contributor.authorShukla, Prashant Kumar
dc.contributor.authorShukla, Piyush Kumar
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorKumar, Manoj
dc.date.accessioned2021-10-28T04:14:36Z
dc.date.available2021-10-28T04:14:36Z
dc.date.issued2021
dc.identifier.issn1996-1073
dc.identifier.doi10.3390/en14206584
dc.identifier.urihttp://hdl.handle.net/10072/409520
dc.description.abstractThe real-time application research on the Fuzzy Logic Systems (FLSs) and Artificial Neural Networks (ANN) is vast and, in this paper, a technique for a photovoltaic failure analysis using the type 2 FLS and ANN is proposed. The method is proposed to build T2 FLS with a guaranteed value equal to or lower than T2 and ANN. Several explanations are conducted to illustrate the effectiveness of the methodologies. It is found that both the type 2 Fuzzy and ANN can be configured for productive actions in applications for a PV fault analysis, and choice is typically applied. The methods discussed in this paper lay the groundwork for developing FLSs and ANNs with durable characteristics that will be extremely useful in many functional applications. The result demonstrates that specific fault categories can be detected using the fault identification method, such as damaged PV modules and partial PV unit shades. The average detection performance is similar in both ANN and fuzzy techniques. In comparison, both systems evaluated show approximately the same performance during experiments. The architecture of the type 2 fuzzy logic system and ANN with radial basic function, including the roles of the output port and the rules for identifying the type of defect in the PV structure is slightly different.
dc.description.peerreviewedYes
dc.languageen
dc.publisherMDPI AG
dc.relation.ispartofpagefrom6584
dc.relation.ispartofissue20
dc.relation.ispartofjournalEnergies
dc.relation.ispartofvolume14
dc.subject.fieldofresearchSoftware engineering
dc.subject.fieldofresearchBuilt environment and design
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchPhysical sciences
dc.subject.fieldofresearchcode4612
dc.subject.fieldofresearchcode33
dc.subject.fieldofresearchcode40
dc.subject.fieldofresearchcode51
dc.titleIntelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationJanarthanan, R; Maheshwari, RU; Shukla, PK; Shukla, PK; Mirjalili, S; Kumar, M, Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems, Energies, 14 (20), pp. 6584
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-10-26T22:43:08Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


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