dc.contributor.author | Janarthanan, Ramadoss | |
dc.contributor.author | Maheshwari, R Uma | |
dc.contributor.author | Shukla, Prashant Kumar | |
dc.contributor.author | Shukla, Piyush Kumar | |
dc.contributor.author | Mirjalili, Seyedali | |
dc.contributor.author | Kumar, Manoj | |
dc.date.accessioned | 2021-10-28T04:14:36Z | |
dc.date.available | 2021-10-28T04:14:36Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 1996-1073 | |
dc.identifier.doi | 10.3390/en14206584 | |
dc.identifier.uri | http://hdl.handle.net/10072/409520 | |
dc.description.abstract | The 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.peerreviewed | Yes | |
dc.language | en | |
dc.publisher | MDPI AG | |
dc.relation.ispartofpagefrom | 6584 | |
dc.relation.ispartofissue | 20 | |
dc.relation.ispartofjournal | Energies | |
dc.relation.ispartofvolume | 14 | |
dc.subject.fieldofresearch | Software engineering | |
dc.subject.fieldofresearch | Built environment and design | |
dc.subject.fieldofresearch | Engineering | |
dc.subject.fieldofresearch | Physical sciences | |
dc.subject.fieldofresearchcode | 4612 | |
dc.subject.fieldofresearchcode | 33 | |
dc.subject.fieldofresearchcode | 40 | |
dc.subject.fieldofresearchcode | 51 | |
dc.title | Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Janarthanan, 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.license | http://creativecommons.org/licenses/by/4.0/ | |
dc.date.updated | 2021-10-26T22:43:08Z | |
dc.description.version | Version 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.hasfulltext | Full Text | |
gro.griffith.author | Mirjalili, Seyedali | |