IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i21p7978-d954921.html
   My bibliography  Save this article

Improved Semi-Supervised Data-Mining-Based Schemes for Fault Detection in a Grid-Connected Photovoltaic System

Author

Listed:
  • Benamar Bouyeddou

    (LESM Lab., Faculty of Technology, University of Saida-Dr Moulay Tahar, Saida 20000, Algeria
    STIC Lab., Department of Telecommunications, Abou Bekr Belkaid University, Tlemcen 13000, 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)

  • 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 Photovoltaique, 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)

  • Amar Hadj Arab

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

Abstract

Fault detection is a necessary component to perform ongoing monitoring of photovoltaic plants and helps in their safety, maintainability, and productivity with the desired performance. In this study, an innovative technique is introduced by amalgamating Latent Variable Regression (LVR) methods, namely Principal Component Regression (PCR) and Partial Least Square (PLS), and the Triple Exponentially Weighted Moving Average (TEWMA) statistical monitoring scheme. The TEWMA scheme is known for its sensitivity to uncovering changes of small magnitude. Nevertheless, TEWMA can only be utilized for monitoring single variables and ignoring the correlation among monitored variables. To alleviate this difficulty, the LVR methods (i.e., PCR and PLS) are used as residual generators. Then, the TEWMA is applied to the obtained residuals for fault detection purposes, where the detection threshold is computed via kernel density estimation to improve its performance and widen its applicability in practice. Real data with different fault scenarios from a 9.54 kW photovoltaic plant has been used to verify the efficiency of the proposed schemes. Results revealed the superior performance of the PLS-TEWMA chart compared to the PLS-TEWMA chart, particularly in detecting anomalies with small changes. Moreover, they have almost comparable performance for large anomalies.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7978-:d:954921
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/21/7978/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/21/7978/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Waqas Ahmed & Jamil Ahmed Sheikh & Shahjadi Hisan Farjana & M. A. Parvez Mahmud, 2021. "Defects Impact on PV System GHG Mitigation Potential and Climate Change," Sustainability, MDPI, vol. 13(14), pages 1-9, July.
    4. 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.
    5. Mugdadi, A. R. & Ahmad, Ibrahim A., 2004. "A bandwidth selection for kernel density estimation of functions of random variables," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 49-62, August.
    6. Fengxin Cui & Yanzhao Tu & Wei Gao, 2022. "A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks," Energies, MDPI, vol. 15(11), pages 1-20, May.
    7. 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.
    8. 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.
    9. Tito G. Amaral & Vitor Fernão Pires & Armando J. Pires, 2021. "Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA," Energies, MDPI, vol. 14(21), pages 1-18, November.
    10. Wang, Wu & Harrou, Fouzi & Bouyeddou, Benamar & Senouci, Sidi-Mohammed & Sun, Ying, 2022. "Cyber-attacks detection in industrial systems using artificial intelligence-driven methods," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    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. 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.
    2. Chiwu Bu & Tao Liu & Tao Wang & Hai Zhang & Stefano Sfarra, 2023. "A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images," Energies, MDPI, vol. 16(9), pages 1-13, April.
    3. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.
    4. 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.
    5. Fouzi Harrou & Ying Sun & Bilal Taghezouit & Abdelkader Dairi, 2023. "Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting," Energies, MDPI, vol. 16(18), pages 1-5, September.
    6. Sairam, Seshapalli & Seshadhri, Subathra & Marafioti, Giancarlo & Srinivasan, Seshadhri & Mathisen, Geir & Bekiroglu, Korkut, 2022. "Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes," Renewable Energy, Elsevier, vol. 185(C), pages 1425-1440.
    7. Tadeusz Olejarz & Dominika Siwiec & Andrzej Pacana, 2022. "Method of Qualitative–Environmental Choice of Devices Converting Green Energy," Energies, MDPI, vol. 15(23), pages 1-22, November.
    8. Li, Yuanliang & Ding, Kun & Zhang, Jingwei & Chen, Fudong & Chen, Xiang & Wu, Jiabing, 2019. "A fault diagnosis method for photovoltaic arrays based on fault parameters identification," Renewable Energy, Elsevier, vol. 143(C), pages 52-63.
    9. 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.
    10. Fang, Xiaoyu & Qu, Jianfeng & Chai, Yi, 2023. "Self-supervised intermittent fault detection for analog circuits guided by prior knowledge," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    11. Robert Ulewicz & Dominika Siwiec & Andrzej Pacana, 2023. "A New Model of Pro-Quality Decision Making in Terms of Products’ Improvement Considering Customer Requirements," Energies, MDPI, vol. 16(11), pages 1-22, May.
    12. Laifa Tao & Haifei Liu & Jiqing Zhang & Xuanyuan Su & Shangyu Li & Jie Hao & Chen Lu & Mingliang Suo & Chao Wang, 2022. "Associated Fault Diagnosis of Power Supply Systems Based on Graph Matching: A Knowledge and Data Fusion Approach," Mathematics, MDPI, vol. 10(22), pages 1-28, November.
    13. Martin Libra & Milan Daneček & Jan Lešetický & Vladislav Poulek & Jan Sedláček & Václav Beránek, 2019. "Monitoring of Defects of a Photovoltaic Power Plant Using a Drone," Energies, MDPI, vol. 12(5), pages 1-9, February.
    14. 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.
    15. Li, Chenxi & Yang, Yongheng & Spataru, Sergiu & Zhang, Kanjian & Wei, Haikun, 2021. "A robust parametrization method of photovoltaic modules for enhancing one-diode model accuracy under varying operating conditions," Renewable Energy, Elsevier, vol. 168(C), pages 764-778.
    16. Sunme Park & Soyeong Park & Myungsun Kim & Euiseok Hwang, 2020. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems," Energies, MDPI, vol. 13(3), pages 1-16, February.
    17. Mohamed Benghanem & Adel Mellit & Chourouk Moussaoui, 2023. "Embedded Hybrid Model (CNN–ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    18. 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.
    19. Srivastava, Chetan & Tripathy, Manoj, 2021. "DC microgrid protection issues and schemes: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    20. Yu Fujimoto & Akihisa Kaneko & Yutaka Iino & Hideo Ishii & Yasuhiro Hayashi, 2023. "Challenges in Smartizing Operational Management of Functionally-Smart Inverters for Distributed Energy Resources: A Review on Machine Learning Aspects," Energies, MDPI, vol. 16(3), pages 1-26, January.

    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:15:y:2022:i:21:p:7978-:d:954921. 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.