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Online fault detection and the economic analysis of grid-connected photovoltaic systems

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  • Madeti, Siva Ramakrishna
  • Singh, S.N.

Abstract

Monitoring system is essential to maintain optimal performance of photovoltaic systems. An automatic fault diagnosis technique in a monitoring system plays critical role in detecting causes affecting the energy production. This paper proposes a new fault detection technique that analyzes the anomalies observed in terminal characteristics of faulty PV strings and corresponding array. The terminal voltage difference between the module in healthy string and the healthy module in unhealthy string can be employed to locate faulty modules. The main advantage of proposed approach is that it voids the need of string current sensors, and reduces the number of voltage sensors using optimal location of voltage sensors. In this technique, power line communication technology based data transmission is used to monitor each PV module. Additionally, a user friendly web application is developed for easy access of monitored data via Internet. Moreover, an economic analysis has also been carried out to study the cost effectiveness of the proposed fault detection technique; considering different values of interest rate and energy tariffs. The profitability of installed grid connected photovoltaic system is determined through its parameters net present value and pay-back period. Experimental results are provided to demonstrate the effectiveness of proposed fault detection technique.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:134:y:2017:i:c:p:121-135
    DOI: 10.1016/j.energy.2017.06.005
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    2. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
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    5. 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.
    6. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    7. D’Adamo, Idiano & Falcone, Pasquale Marcello & Gastaldi, Massimo & Morone, Piergiuseppe, 2020. "The economic viability of photovoltaic systems in public buildings: Evidence from Italy," Energy, Elsevier, vol. 207(C).
    8. Jufri, Fauzan Hanif & Oh, Seongmun & Jung, Jaesung, 2019. "Development of Photovoltaic abnormal condition detection system using combined regression and Support Vector Machine," Energy, Elsevier, vol. 176(C), pages 457-467.
    9. Dashti, Rahman & Ghasemi, Mohsen & Daisy, Mohammad, 2018. "Fault location in power distribution network with presence of distributed generation resources using impedance based method and applying π line model," Energy, Elsevier, vol. 159(C), pages 344-360.
    10. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2023. "Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks," Energy, Elsevier, vol. 266(C).
    11. 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.
    12. 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.
    13. Wu, Lijun & Chen, Zhicong & Long, Chao & Cheng, Shuying & Lin, Peijie & Chen, Yixiang & Chen, Huihuang, 2018. "Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm," Applied Energy, Elsevier, vol. 232(C), pages 36-53.
    14. 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.

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