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Takagi-Sugeno Type Neuro Fuzzy System Model Based Fault Diagnostic in Photovoltaic System

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Advances in Computational Intelligence (MICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13613))

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Abstract

Exposing photovoltaic installations to the outdoors for a long time leads to various breakdowns. Accurate and quantitative diagnosis of defect severity is vital for decision-making given the cost of defect removal. In this paper, a Takagi-Sugeno type neuro-fuzzy approach for the detection, identification and location of faults in solar photovoltaic systems is proposed. Then, an algorithm for optimizing the structure of the neuro-fuzzy model is developed, in order to improve the convergence capacity, resolve the influence of random parameters on the predictive performances, and to reduce both the number of synaptic weights and membership functions. This approach accurately detects, identifies and locates photovoltaic faults based on measurements of fault current, photovoltaic voltage, irradiance level, temperature and weather. Various fault scenarios are experimentally verified and validated in a large-scale solar photovoltaic power plant in order to demonstrate the efficiency and accuracy of the proposed approach.

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Authors and Affiliations

  1. Department of Applied Physics, Faculty of Science and Technology, Cadi Ayyad University, Marrakesh, Morocco

    Moulay Rachid Douiri & Noureddine Aouzale

Authors
  1. Moulay Rachid Douiri
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  2. Noureddine Aouzale
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Correspondence to Moulay Rachid Douiri .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico, Mexico

    Obdulia Pichardo Lagunas

  2. Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, Mexico

    Juan Martínez-Miranda

  3. Instituto Politécnico Nacional, Mexico, Mexico

    Bella Martínez Seis

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Douiri, M.R., Aouzale, N. (2022). Takagi-Sugeno Type Neuro Fuzzy System Model Based Fault Diagnostic in Photovoltaic System. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13613. Springer, Cham. https://doi.org/10.1007/978-3-031-19496-2_29

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  • DOI: https://doi.org/10.1007/978-3-031-19496-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19495-5

  • Online ISBN: 978-3-031-19496-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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