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Article

Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems

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Center for Artificial Intelligence, Department of CSE, Chennai Institute of Technology, Chennai 600069, Tamil Nadu, India
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Department of ECE, Hindustan Institute of Technology, Coimbatore 641028, Tamil Nadu, India
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Department of Computer Science and Engineering, K L University, Vijayawada 520002, Andhra Pradesh, India
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Computer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Technological University of Madhya Pradesh, Bhopal 462023, Madhya Pradesh, India
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Center for Artificial Intelligence and Optimization, Torrens University Australia, Brisbane, QLD 4006, Australia
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Yonsei Frontier Laboratory, Yonsei University, Seoul 03722, Korea
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School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India
*
Authors to whom correspondence should be addressed.
Academic Editors: Amirmehdi Yazdani, Amin Mahmoudi and GM Shafiullah
Energies 2021, 14(20), 6584; https://doi.org/10.3390/en14206584
Received: 11 September 2021 / Revised: 30 September 2021 / Accepted: 5 October 2021 / Published: 13 October 2021
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.
Keywords: type 2 fuzzy logic systems; artificial neural network; machine learning; photovoltaic (PV) fault detection type 2 fuzzy logic systems; artificial neural network; machine learning; photovoltaic (PV) fault detection
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MDPI and ACS Style

Janarthanan, R.; Maheshwari, R.U.; Shukla, P.K.; Shukla, P.K.; Mirjalili, S.; Kumar, M. Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems. Energies 2021, 14, 6584. https://doi.org/10.3390/en14206584

AMA Style

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. 2021; 14(20):6584. https://doi.org/10.3390/en14206584

Chicago/Turabian Style

Janarthanan, Ramadoss, R. U. Maheshwari, Prashant K. Shukla, Piyush K. Shukla, Seyedali Mirjalili, and Manoj Kumar. 2021. "Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems" Energies 14, no. 20: 6584. https://doi.org/10.3390/en14206584

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