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, Manoj Kumar

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


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.

Original languageEnglish
Article number6584
Issue number20
Publication statusPublished - 1 Oct 2021


  • Artificial neural network
  • Machine learning
  • Photovoltaic (PV) fault detection
  • Type 2 fuzzy logic systems


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