Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems Article Swipe
R. Janarthanan
,
Rahul Maheshwari
,
Prashant Kumar Shukla
,
Piyush Kumar Shukla
,
Seyedali Mirjalili
,
Manoj Kumar
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/en14206584
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/en14206584
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.
Related Topics
Concepts
Artificial neural network
Fuzzy logic
Photovoltaic system
Fault detection and isolation
Fault (geology)
Computer science
Neuro-fuzzy
Artificial intelligence
Identification (biology)
Engineering
Control engineering
Fuzzy control system
Machine learning
Electrical engineering
Geology
Biology
Actuator
Botany
Seismology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en14206584
- https://www.mdpi.com/1996-1073/14/20/6584/pdf?version=1634565466
- OA Status
- gold
- Cited By
- 91
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3205379079
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3205379079Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en14206584Digital Object Identifier
- Title
-
Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy SystemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-13Full publication date if available
- Authors
-
R. Janarthanan, Rahul Maheshwari, Prashant Kumar Shukla, Piyush Kumar Shukla, Seyedali Mirjalili, Manoj KumarList of authors in order
- Landing page
-
https://doi.org/10.3390/en14206584Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/14/20/6584/pdf?version=1634565466Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1996-1073/14/20/6584/pdf?version=1634565466Direct OA link when available
- Concepts
-
Artificial neural network, Fuzzy logic, Photovoltaic system, Fault detection and isolation, Fault (geology), Computer science, Neuro-fuzzy, Artificial intelligence, Identification (biology), Engineering, Control engineering, Fuzzy control system, Machine learning, Electrical engineering, Geology, Biology, Actuator, Botany, SeismologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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91Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 22, 2024: 19, 2023: 14, 2022: 36Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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