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

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 l...

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Veröffentlicht in:Energies (Basel) 2021-10, Vol.14 (20), p.6584, Article 6584
Hauptverfasser: Janarthanan, Ramadoss, Maheshwari, R. Uma, Shukla, Prashant Kumar, Shukla, Piyush Kumar, Mirjalili, Seyedali, Kumar, Manoj
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container_start_page 6584
container_title Energies (Basel)
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creator Janarthanan, Ramadoss
Maheshwari, R. Uma
Shukla, Prashant Kumar
Shukla, Piyush Kumar
Mirjalili, Seyedali
Kumar, Manoj
description 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.
doi_str_mv 10.3390/en14206584
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subjects Algorithms
Arrays
artificial neural network
Efficiency
Electricity
Energy & Fuels
Fault detection
Fuzzy logic
Fuzzy systems
Identification methods
Machine learning
Methods
Neural networks
photovoltaic (PV) fault detection
Photovoltaic cells
Photovoltaics
Radiation
Science & Technology
Shades
Systems analysis
Technology
type 2 fuzzy logic systems
title Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems
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