IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i18p6746-d1244919.html
   My bibliography  Save this article

Application of the Energy Efficiency Mathematical Model to Diagnose Photovoltaic Micro-Systems

Author

Listed:
  • Wiktor Olchowik

    (Division of Electronic Systems Exploitations, Institute of Electronic Systems, Faculty of Electronics, Military University of Technology, 2 Gen. S. Kaliski St., 00-908 Warsaw, Poland)

  • Marcin Bednarek

    (Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 12 Powstańców Warszawy Ave, 35-959 Rzeszów, Poland)

  • Tadeusz Dąbrowski

    (Division of Electronic Systems Exploitations, Institute of Electronic Systems, Faculty of Electronics, Military University of Technology, 2 Gen. S. Kaliski St., 00-908 Warsaw, Poland)

  • Adam Rosiński

    (Division of Air Transport Engineering and Teleinformatics, Faculty of Transport, Warsaw University of Technology, 75 Koszykowa St., 00-662 Warsaw, Poland)

Abstract

The intensive development of photovoltaic (PV) micro-systems contributes to increased interest in energy efficiency and diagnosing the condition of such solutions. Optimizing system energy efficiency and servicing costs are particularly noteworthy among the numerous issues associated with this topic. This research paper addresses the easy and reliable diagnosis of PV system malfunctions. It discusses the original PV system energy efficiency simulation model with proprietary methods for determining total solar irradiance on the plane of cells installed at any inclination angle and azimuth, as well as PV cell temperature and efficiency as a function of solar irradiance. Based on this simulation model, the authors developed procedures for the remote diagnosis of PV micro-systems. Verification tests covered two independent PV systems over the period from April 2022 to May 2023. The obtained results confirm the high credibility level of both the adopted energy efficiency simulation model and the proposed method for diagnosing PV system functional status.

Suggested Citation

  • Wiktor Olchowik & Marcin Bednarek & Tadeusz Dąbrowski & Adam Rosiński, 2023. "Application of the Energy Efficiency Mathematical Model to Diagnose Photovoltaic Micro-Systems," Energies, MDPI, vol. 16(18), pages 1-24, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6746-:d:1244919
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/18/6746/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/18/6746/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guanyi Yu & Weidong Chen & Junnan Wang & Yumeng Hu, 2022. "Research on Decision-Making for a Photovoltaic Power Generation Business Model under Integrated Energy Services," Energies, MDPI, vol. 15(15), pages 1-12, August.
    2. Muhammad Hussain & Hussain Al-Aqrabi & Richard Hill, 2022. "Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection," Energies, MDPI, vol. 15(15), pages 1-14, July.
    3. Karolina Krzykowska-Piotrowska & Ewa Dudek & Paweł Wielgosz & Beata Milanowska & Jordi Mongay Batalla, 2021. "On the Correlation of Solar Activity and Troposphere on the GNSS/EGNOS Integrity. Fuzzy Logic Approach," Energies, MDPI, vol. 14(15), pages 1-19, July.
    4. João Paulo N. Torres & Ricardo A. Marques Lameirinhas & Catarina P. Correia V. Bernardo & Helena Isabel Veiga & Pedro Mendonça dos Santos, 2023. "A Discrete Electrical Model for Photovoltaic Solar Cells—d1MxP," Energies, MDPI, vol. 16(4), pages 1-14, February.
    5. Mellit, Adel & Kalogirou, Soteris, 2022. "Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems," Renewable Energy, Elsevier, vol. 184(C), pages 1074-1090.
    6. Faiçal Hamidi & Severus Constantin Olteanu & Dumitru Popescu & Houssem Jerbi & Ingrid Dincă & Sondess Ben Aoun & Rabeh Abbassi, 2020. "Model Based Optimisation Algorithm for Maximum Power Point Tracking in Photovoltaic Panels," Energies, MDPI, vol. 13(18), pages 1-20, September.
    7. Hay, John E., 1993. "Calculating solar radiation for inclined surfaces: Practical approaches," Renewable Energy, Elsevier, vol. 3(4), pages 373-380.
    8. Aline Kirsten Vidal de Oliveira & Mohammadreza Aghaei & Ricardo Rüther, 2022. "Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review," Energies, MDPI, vol. 15(6), pages 1-24, March.
    9. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    10. Krzysztof Zagrajek & Mariusz Kłos & Desire D. Rasolomampionona & Mirosław Lewandowski & Karol Pawlak & Łukasz Baran & Tomasz Barcz & Przemysław Kołaczyński & Wojciech Suchecki, 2023. "Investing in Distributed Generation Technologies at Polish University Campuses during the Energy Transition Era," Energies, MDPI, vol. 16(12), pages 1-24, June.
    11. 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.
    12. Grzegorz Trzmiel & Jaroslaw Jajczyk & Ewa Kardas-Cinal & Norbert Chamier-Gliszczynski & Waldemar Wozniak & Konrad Lewczuk, 2021. "The Condition of Photovoltaic Modules under Random Operation Parameters," Energies, MDPI, vol. 14(24), pages 1-18, December.
    13. Arévalo, Paul & Benavides, Dario & Tostado-Véliz, Marcos & Aguado, José A. & Jurado, Francisco, 2023. "Smart monitoring method for photovoltaic systems and failure control based on power smoothing techniques," Renewable Energy, Elsevier, vol. 205(C), pages 366-383.
    14. Srinivasan Alwar & Devakirubakaran Samithas & Meenakshi Sundaram Boominathan & Praveen Kumar Balachandran & Lucian Mihet-Popa, 2022. "Performance Analysis of Thermal Image Processing-Based Photovoltaic Fault Detection and PV Array Reconfiguration—A Detailed Experimentation," Energies, MDPI, vol. 15(22), pages 1-21, November.
    15. 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.
    16. Norbert Chamier-Gliszczynski & Grzegorz Trzmiel & Jarosław Jajczyk & Aleksandra Juszczak & Waldemar Woźniak & Mariusz Wasiak & Robert Wojtachnik & Krzysztof Santarek, 2023. "The Influence of Distributed Generation on the Operation of the Power System, Based on the Example of PV Micro-Installations," Energies, MDPI, vol. 16(3), pages 1-29, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Piotr Michalak, 2021. "Modelling of Solar Irradiance Incident on Building Envelopes in Polish Climatic Conditions: The Impact on Energy Performance Indicators of Residential Buildings," Energies, MDPI, vol. 14(14), pages 1-27, July.
    2. Jimmy Gallegos & Paul Arévalo & Christian Montaleza & Francisco Jurado, 2024. "Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review," Sustainability, MDPI, vol. 16(2), pages 1-33, January.
    3. João Paulo N. Torres & Ricardo A. Marques Lameirinhas & Catarina Pinho Correia Valério Bernardo & Sofia Lima Martins & Pedro Mendonça dos Santos & Helena Isabel Veiga & Maria João Marques Martins & Pa, 2023. "Analysis of Different Third-Generation Solar Cells Using the Discrete Electrical Model d1MxP," Energies, MDPI, vol. 16(7), pages 1-12, April.
    4. D'Adamo, Idiano & Mammetti, Marco & Ottaviani, Dario & Ozturk, Ilhan, 2023. "Photovoltaic systems and sustainable communities: New social models for ecological transition. The impact of incentive policies in profitability analyses," Renewable Energy, Elsevier, vol. 202(C), pages 1291-1304.
    5. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
    6. Kara Mostefa Khelil, Chérifa & Amrouche, Badia & Benyoucef, Abou soufiane & Kara, Kamel & Chouder, Aissa, 2020. "New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems," Energy, Elsevier, vol. 211(C).
    7. Jasiewicz Jarosław & Cierniewski Jerzy, 2021. "SALBEC – A Python Library and GUI Application to Calculate the Diurnal Variation of the Soil Albedo," Quaestiones Geographicae, Sciendo, vol. 40(3), pages 95-107, September.
    8. Cheng Yang & Fuhao Sun & Yujie Zou & Zhipeng Lv & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Haoyang Cui, 2024. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods," Energies, MDPI, vol. 17(4), pages 1-37, February.
    9. Yu, Cao & Wang, Haizheng & Yao, Jianxi & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2020. "A dynamic alarm threshold setting method for photovoltaic array and its application," Renewable Energy, Elsevier, vol. 158(C), pages 13-22.
    10. Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
    11. Chen, Zhicong & Wu, Lijun & Lin, Peijie & Wu, Yue & Cheng, Shuying, 2016. "Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy," Applied Energy, Elsevier, vol. 182(C), pages 47-57.
    12. Dhimish, Mahmoud & Holmes, Violeta & Dales, Mark, 2017. "Parallel fault detection algorithm for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 113(C), pages 94-111.
    13. 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.
    14. 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.
    15. Casares de la Torre, F.J. & Varo, Marta & López-Luque, R. & Ramírez-Faz, J. & Fernández-Ahumada, L.M., 2022. "Design and analysis of a tracking / backtracking strategy for PV plants with horizontal trackers after their conversion to agrivoltaic plants," Renewable Energy, Elsevier, vol. 187(C), pages 537-550.
    16. 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.
    17. Dimri, Neha & Tiwari, Arvind & Tiwari, G.N., 2019. "Comparative study of photovoltaic thermal (PVT) integrated thermoelectric cooler (TEC) fluid collectors," Renewable Energy, Elsevier, vol. 134(C), pages 343-356.
    18. Pavel Kuznetsov & Dmitry Kotelnikov & Leonid Yuferev & Vladimir Panchenko & Vadim Bolshev & Marek Jasiński & Aymen Flah, 2022. "Method for the Automated Inspection of the Surfaces of Photovoltaic Modules," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    19. 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.
    20. Fonseca Alves, Ricardo Henrique & Deus Júnior, Getúlio Antero de & Marra, Enes Gonçalves & Lemos, Rodrigo Pinto, 2021. "Automatic fault classification in photovoltaic modules using Convolutional Neural Networks," Renewable Energy, Elsevier, vol. 179(C), pages 502-516.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6746-:d:1244919. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.