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A Cost-Effective Fault Diagnosis and Localization Approach for Utility-Scale PV Systems Using Limited Number of Sensors

Author

Listed:
  • Faris E. Alfaris

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Essam A. Al-Ammar

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Ghazi A. Ghazi

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Ahmed A. AL-Katheri

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

As a result of global efforts to combat the rise in global climate change and carbon dioxide emissions, there has been a substantial increase in renewable energy investment for both residential and utility power generation. Solar power facilities are estimated to be among the major contributors to global decarbonization in terms of capacity by 2050. Consequently, the majority of economically significant countries are progressively implementing utility-scale photovoltaic (U-PV) systems. Nevertheless, a major obstacle to the expansion of U-PV generation is the identification and assessment of direct current (DC) faults in the extensive array of PV panels. In order to address this obstacle, it is imperative to provide an evaluation method that can accurately and cost-effectively identify and locate potential DC faults in PV arrays. Therefore, many studies attempted to utilize thermal cameras, voltage and current sensors, power databases, and other detecting elements; however, some of these technologies provide extra hurdles in terms of the quantity and expense of the utilized hardware equipment. This work presents a sophisticated system that aims to diagnose and locate various types of PV faults, such as line-to-ground, line-to-line, inter-string, open-circuit, and partial shading events, within a PV array strings down to a module level. This study primarily depends on three crucial indicators: precise calculation of the PV array output power and current, optimal placement of a limited number of voltage sensors, and execution of specifically specified tests. The estimation of PV array power, along with selectively placed voltage sensors, minimizes the time and equipment required for fault detection and diagnosis. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate that the proposed approach can accurately diagnose and localize faults with only N S /2 voltage sensors, where N S is the number of PV array parallel strings.

Suggested Citation

  • Faris E. Alfaris & Essam A. Al-Ammar & Ghazi A. Ghazi & Ahmed A. AL-Katheri, 2024. "A Cost-Effective Fault Diagnosis and Localization Approach for Utility-Scale PV Systems Using Limited Number of Sensors," Sustainability, MDPI, vol. 16(15), pages 1-25, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6454-:d:1444754
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    References listed on IDEAS

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    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. Easter Joseph & Pradeep Menon Vijaya Kumar & Balbir Singh Mahinder Singh & Dennis Ling Chuan Ching, 2023. "Performance Monitoring Algorithm for Detection of Encapsulation Failures and Cell Corrosion in PV Modules," Energies, MDPI, vol. 16(8), pages 1-12, April.
    3. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    4. Faris E. Alfaris, 2023. "A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units," Energies, MDPI, vol. 16(3), pages 1-17, January.
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