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

Ramadoss Janarthanan, R. Uma Maheshwari, Prashant Kumar Shukla, Piyush Kumar Shukla, Seyedali Mirjalili and Manoj Kumar
Additional contact information
Ramadoss Janarthanan: Center for Artificial Intelligence, Department of CSE, Chennai Institute of Technology, Chennai 600069, Tamil Nadu, India
R. Uma Maheshwari: Department of ECE, Hindustan Institute of Technology, Coimbatore 641028, Tamil Nadu, India
Prashant Kumar Shukla: Department of Computer Science and Engineering, K L University, Vijayawada 520002, Andhra Pradesh, India
Piyush Kumar Shukla: Computer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Technological University of Madhya Pradesh, Bhopal 462023, Madhya Pradesh, India
Seyedali Mirjalili: Center for Artificial Intelligence and Optimization, Torrens University Australia, Brisbane, QLD 4006, Australia
Manoj Kumar: School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India

Energies, 2021, vol. 14, issue 20, 1-19

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.

Keywords: type 2 fuzzy logic systems; artificial neural network; machine learning; photovoltaic (PV) fault detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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