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Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study

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Listed:
  • Bilal Taghezouit

    (Centre de Développement des Energies Renouvelables, CDER, B.P. 62, Route de l’Observatoire, Algiers 16340, Algeria
    Laboratoire de Dispositifs de Communication et de Conversion Photovoltaïque, Ecole Nationale Polytechnique Alger, Algiers 16200, Algeria)

  • Fouzi Harrou

    (Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia)

  • Cherif Larbes

    (Laboratoire de Dispositifs de Communication et de Conversion Photovoltaïque, Ecole Nationale Polytechnique Alger, Algiers 16200, Algeria)

  • Ying Sun

    (Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia)

  • Smail Semaoui

    (Centre de Développement des Energies Renouvelables, CDER, B.P. 62, Route de l’Observatoire, Algiers 16340, Algeria)

  • Amar Hadj Arab

    (Centre de Développement des Energies Renouvelables, CDER, B.P. 62, Route de l’Observatoire, Algiers 16340, Algeria)

  • Salim Bouchakour

    (Centre de Développement des Energies Renouvelables, CDER, B.P. 62, Route de l’Observatoire, Algiers 16340, Algeria)

Abstract

The capacity of photovoltaic solar power installations has been boosted last years by reaching a new record with 175 GWdc of newly installed solar power in 2021. To guarantee reliable performances of photovoltaic (PV) plants and maintain target requirements, faults have to be reliably detected and diagnosed. A method for an effective and reliable fault diagnosis of PV plants based on the behavioral model and performance analysis under the LabVIEW environment is presented in this paper. Specifically, the first phase of this study consists of the behavioral modeling of the PV array and the inverter in order to estimate the electricity production and analyze the performance of the 9.54 kWp Grid Connected PV System (GCPVS). Here, the results obtained from the empirical models were validated and calibrated by experimental data. Furthermore, a user interface for modeling and analyzing the performance of a PV system under LabVIEW has been designed. The second phase of this work is dedicated to the design of a simple and efficient diagnostic tool in order to detect and recognize faults occurring in the PV systems. Essentially, the residuals obtained using the parametric models are analyzed via the performance loss rates (PLR) of four electrical indicators (i.e., DC voltage, DC current, DC power, and AC power). To evaluate the proposed method, numerous environmental anomalies and electrical faults affecting the GCPVS were taken into account. Results demonstrated the satisfactory prediction performance of the considered empirical models to predict the considered variables, including DC current, DC power, and AC power with an R 2 of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved.

Suggested Citation

  • Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7955-:d:954267
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    References listed on IDEAS

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    1. Salim Bouchakour & Daniel Valencia-Caballero & Alvaro Luna & Eduardo Roman & El Amin Kouadri Boudjelthia & Pedro Rodríguez, 2021. "Modelling and Simulation of Bifacial PV Production Using Monofacial Electrical Models," Energies, MDPI, vol. 14(14), pages 1-16, July.
    2. Yao Wang & Cuiyan Bai & Xiaopeng Qian & Wanting Liu & Chen Zhu & Leijiao Ge, 2022. "A DC Series Arc Fault Detection Method Based on a Lightweight Convolutional Neural Network Used in Photovoltaic System," Energies, MDPI, vol. 15(8), pages 1-20, April.
    3. Harrou, Fouzi & Sun, Ying & Taghezouit, Bilal & Saidi, Ahmed & Hamlati, Mohamed-Elkarim, 2018. "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, Elsevier, vol. 116(PA), pages 22-37.
    4. Tingting Pei & Xiaohong Hao, 2019. "A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation," Energies, MDPI, vol. 12(9), pages 1-16, May.
    5. Meng-Hui Wang & Zong-Han Lin & Shiue-Der Lu, 2022. "A Fault Detection Method Based on CNN and Symmetrized Dot Pattern for PV Modules," Energies, MDPI, vol. 15(17), pages 1-17, September.
    6. Roberto Pierdicca & Marina Paolanti & Andrea Felicetti & Fabio Piccinini & Primo Zingaretti, 2020. "Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images," Energies, MDPI, vol. 13(24), pages 1-17, December.
    7. 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.
    8. Rawat, Rahul & Kaushik, S.C. & Lamba, Ravita, 2016. "A review on modeling, design methodology and size optimization of photovoltaic based water pumping, standalone and grid connected system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1506-1519.
    9. Blaifi, Sid-ali & Moulahoum, Samir & Taghezouit, Bilal & Saim, Abdelhakim, 2019. "An enhanced dynamic modeling of PV module using Levenberg-Marquardt algorithm," Renewable Energy, Elsevier, vol. 135(C), pages 745-760.
    10. Fouzi Harrou & Bilal Taghezouit & Sofiane Khadraoui & Abdelkader Dairi & Ying Sun & Amar Hadj Arab, 2022. "Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems," Energies, MDPI, vol. 15(18), pages 1-28, September.
    11. Chao-Ming Huang & Shin-Ju Chen & Sung-Pei Yang, 2022. "A Parameter Estimation Method for a Photovoltaic Power Generation System Based on a Two-Diode Model," Energies, MDPI, vol. 15(4), pages 1-16, February.
    12. 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.
    13. Massi Pavan, A. & Mellit, A. & De Pieri, D. & Kalogirou, S.A., 2013. "A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants," Applied Energy, Elsevier, vol. 108(C), pages 392-401.
    14. Jingwei Zhang & Zenan Yang & Kun Ding & Li Feng & Frank Hamelmann & Xihui Chen & Yongjie Liu & Ling Chen, 2022. "Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics," Energies, MDPI, vol. 15(18), pages 1-17, September.
    15. 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.
    16. Benkercha, Rabah & Moulahoum, Samir & Taghezouit, Bilal, 2019. "Extraction of the PV modules parameters with MPP estimation using the modified flower algorithm," Renewable Energy, Elsevier, vol. 143(C), pages 1698-1709.
    17. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
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