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Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems

Author

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  • Sufyan Samara

    (Department of Computer Engineering, An-Najah National University, PO Box 7, Nablus, Palestine)

  • Emad Natsheh

    (Department of Computer Engineering, An-Najah National University, PO Box 7, Nablus, Palestine)

Abstract

The expanding use of photovoltaic (PV) systems as an alternative green source for electricity presents many challenges, one of which is the timely diagnosis of faults to maintain the quality and high productivity of such systems. In recent years, various studies have been conducted on the fault diagnosis of PV systems. However, very few instances of fault diagnostic techniques could be implemented on integrated circuits, and these techniques require costly and complex hardware. This work presents a novel and effective, yet small and implementable, fault diagnosis algorithm based on an artificial intelligent nonlinear autoregressive exogenous (NARX) neural network and Sugeno fuzzy inference. The algorithm uses Sugeno fuzzy inference to isolate and classify faults that may occur in a PV system. The fuzzy inference requires the actual sensed PV system output power, the predicted PV system output power, and the sensed surrounding conditions. An artificial intelligent NARX-based neural network is used to obtain the predicted PV system output power. The actual output power of the PV system and the surrounding conditions are obtained in real-time using sensors. The algorithm is proven to be implementable on a low-cost microcontroller. The obtained results indicate that the fault diagnosis algorithm can detect multiple faults such as open and short circuit degradation, faulty maximum power point tracking (MPPT), and conditions of partial shading (PS) that may affect the PV system. Moreover, radiation and temperature, among other non-linear associations of patterns between predictors, can be captured by the proposed algorithm to determine the accurate point of the maximum power for the PV system.

Suggested Citation

  • Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:2011-:d:329054
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    1. Olivencia Polo, Fernando A. & Ferrero Bermejo, Jesús & Gómez Fernández, Juan F. & Crespo Márquez, Adolfo, 2015. "Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models," Renewable Energy, Elsevier, vol. 81(C), pages 227-238.
    2. Li, Guiqiang & Jin, Yi & Akram, M.W. & Chen, Xiao & Ji, Jie, 2018. "Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 840-873.
    3. Mansouri, Majdi & Hajji, Mansour & Trabelsi, Mohamed & Harkat, Mohamed Faouzi & Al-khazraji, Ayman & Livera, Andreas & Nounou, Hazem & Nounou, Mohamed, 2018. "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test," Energy, Elsevier, vol. 159(C), pages 842-856.
    4. Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Mather, Peter, 2017. "Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system," Energy, Elsevier, vol. 140(P1), pages 276-290.
    5. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
    6. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
    7. 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.
    8. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
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    2. Juan M. Cano & Aranzazu D. Martin & Reyes S. Herrera & Jesus R. Vazquez & Francisco Javier Ruiz-Rodriguez, 2021. "Grid-Connected PV Systems Controlled by Sliding via Wireless Communication," Energies, MDPI, vol. 14(7), pages 1-17, March.
    3. Qamar Navid & Ahmed Hassan & Abbas Ahmad Fardoun & Rashad Ramzan, 2020. "An Online Novel Two-Layered Photovoltaic Fault Monitoring Technique Based Upon the Thermal Signatures," Sustainability, MDPI, vol. 12(22), pages 1-13, November.
    4. Adel Mellit & Omar Herrak & Catalina Rus Casas & Alessandro Massi Pavan, 2021. "A Machine Learning and Internet of Things-Based Online Fault Diagnosis Method for Photovoltaic Arrays," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    5. Christopher Gradwohl & Vesna Dimitrievska & Federico Pittino & Wolfgang Muehleisen & András Montvay & Franz Langmayr & Thomas Kienberger, 2021. "A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic," Energies, MDPI, vol. 14(5), pages 1-23, February.

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