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

Author

Listed:
  • 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
    Yonsei Frontier Laboratory, Yonsei University, Seoul 03722, Korea)

  • Manoj Kumar

    (School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India)

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.

Suggested Citation

  • Ramadoss Janarthanan & R. Uma Maheshwari & Prashant Kumar Shukla & Piyush Kumar Shukla & Seyedali Mirjalili & Manoj Kumar, 2021. "Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems," Energies, MDPI, vol. 14(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6584-:d:654997
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    References listed on IDEAS

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    1. Madeti, Siva Ramakrishna & Singh, S.N., 2017. "Online fault detection and the economic analysis of grid-connected photovoltaic systems," Energy, Elsevier, vol. 134(C), pages 121-135.
    2. Qiang Zhao & Shuai Shao & Lingxing Lu & Xin Liu & Honglu Zhu, 2018. "A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm," Energies, MDPI, vol. 11(1), pages 1-21, January.
    3. Dhimish, Mahmoud & Badran, Ghadeer, 2020. "Current limiter circuit to avoid photovoltaic mismatch conditions including hot-spots and shading," Renewable Energy, Elsevier, vol. 145(C), pages 2201-2216.
    4. Akram, M. Waqar & Li, Guiqiang & Jin, Yi & Chen, Xiao & Zhu, Changan & Zhao, Xudong & Khaliq, Abdul & Faheem, M. & Ahmad, Ashfaq, 2019. "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, Elsevier, vol. 189(C).
    5. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
    6. Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
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    Cited by:

    1. Benamar Bouyeddou & Fouzi Harrou & Bilal Taghezouit & Ying Sun & Amar Hadj Arab, 2022. "Improved Semi-Supervised Data-Mining-Based Schemes for Fault Detection in a Grid-Connected Photovoltaic System," Energies, MDPI, vol. 15(21), pages 1-22, October.
    2. Tawfiq Aljohani, 2024. "Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration," Energies, MDPI, vol. 17(12), pages 1-26, June.

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