IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v211y2018icp568-581.html
   My bibliography  Save this article

Sparse voltage amplitude measurement based fault location in large-scale photovoltaic power plants

Author

Listed:
  • Jia, Ke
  • Gu, Chenjie
  • Li, Lun
  • Xuan, Zhengwen
  • Bi, Tianshu
  • Thomas, David

Abstract

Large-scale photovoltaic (PV) power plants contain numerous transmission line branches and laterals inside. When a fault occurs conventional fault location methods face challenges due to the complex system structure and the diversity of PV inverter controls. Most of the published fault location methods cannot be directly used in the PV power plant due to the following issues: (1) Most of the fault location methods consider the PV inverter as a constant voltage source while the actual inverters have varied controls during faults. Without analysis of the unique fault transients of the PV, the fault location will suffer from errors. (2) In a complicated large-scale PV power plant with massive quantity of nodes, the synchronised measurements from all the nodes are almost impossible. A method with sparse un-synchronized measurements is required. Therefore, a new negative-sequence voltage amplitude sparse measurement based fault location method is proposed for unbalanced faults. The improved Bayesian compressive sensing algorithm is used to recover the sparse fault current vector and then determine the faulted node. Both the field testing and the simulation results indicate that the proposed method can locate the faulted nodes accurately and effectively without synchronizing measurement requirements from all the nodes. This method also presents a good performance for various unbalanced fault types, fault resistances, inverter controls and signal noise. All these factors make the propose method feasible for industrial applications.

Suggested Citation

  • Jia, Ke & Gu, Chenjie & Li, Lun & Xuan, Zhengwen & Bi, Tianshu & Thomas, David, 2018. "Sparse voltage amplitude measurement based fault location in large-scale photovoltaic power plants," Applied Energy, Elsevier, vol. 211(C), pages 568-581.
    • Handle: RePEc:eee:appene:v:211:y:2018:i:c:p:568-581
    DOI: 10.1016/j.apenergy.2017.11.075
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917316744
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.11.075?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Badihi, Hamed & Zhang, Youmin & Hong, Henry, 2017. "Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches," Applied Energy, Elsevier, vol. 201(C), pages 284-307.
    2. El Halabi, N. & García-Gracia, M. & Borroy, J. & Villa, J.L., 2011. "Current phase comparison pilot scheme for distributed generation networks protection," Applied Energy, Elsevier, vol. 88(12), pages 4563-4569.
    3. Gururajapathy, S.S. & Mokhlis, H. & Illias, H.A., 2017. "Fault location and detection techniques in power distribution systems with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 949-958.
    4. Mirsaeidi, Sohrab & Said, Dalila Mat & Mustafa, Mohammad Wazir & Habibuddin, Mohammad Hafiz & Ghaffari, Kimia, 2016. "Fault location and isolation in micro-grids using a digital central protection unit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1-17.
    5. Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    2. Dey, Maitreyee & Rana, Soumya Prakash & Simmons, Clarke V. & Dudley, Sandra, 2021. "Solar farm voltage anomaly detection using high-resolution μPMU data-driven unsupervised machine learning," Applied Energy, Elsevier, vol. 303(C).
    3. Xiangyu Kong & Xiaoxiao Yuan & Yuting Wang & Yong Xu & Li Yu, 2019. "Research on Optimal D-PMU Placement Technology to Improve the Observability of Smart Distribution Networks," Energies, MDPI, vol. 12(22), pages 1-23, November.
    4. Sun, Chenhao & Wang, Xin & Zheng, Yihui, 2020. "An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks," Applied Energy, Elsevier, vol. 258(C).
    5. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
    6. Jiang Li & Wenzhen Wei & Shuo Zhang & Guoqing Li & Chenghong Gu, 2018. "Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks," Energies, MDPI, vol. 11(5), pages 1-18, May.
    7. Zhao, Zhida & Yu, Hao & Li, Peng & Li, Peng & Kong, Xiangyu & Wu, Jianzhong & Wang, Chengshan, 2019. "Optimal placement of PMUs and communication links for distributed state estimation in distribution networks," Applied Energy, Elsevier, vol. 256(C).
    8. Su, Hongzhi & Wang, Chengshan & Li, Peng & Li, Peng & Liu, Zhelin & Wu, Jianzhong, 2019. "Novel voltage-to-power sensitivity estimation for phasor measurement unit-unobservable distribution networks based on network equivalent," Applied Energy, Elsevier, vol. 250(C), pages 302-312.
    9. Fanjie Yang & Yun Zeng & Jing Qian & Youtao Li & Shihao Xie, 2023. "Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm," Energies, MDPI, vol. 16(3), pages 1-19, January.
    10. Bingjie Zhai & Kaijian Ou & Yuhong Wang & Tian Cao & Huaqing Dai & Zongsheng Zheng, 2024. "Parameter Identification of PMSG-Based Wind Turbine Based on Sensitivity Analysis and Improved Gray Wolf Optimization," Energies, MDPI, vol. 17(17), pages 1-15, August.

    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. Tang, Liangyu & Han, Yang & Zalhaf, Amr S. & Zhou, Siyu & Yang, Ping & Wang, Congling & Huang, Tao, 2024. "Resilience enhancement of active distribution networks under extreme disaster scenarios: A comprehensive overview of fault location strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    2. Saeid Khavari & Rahman Dashti & Hamid Reza Shaker & Athila Santos, 2020. "High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers," Energies, MDPI, vol. 13(9), pages 1-15, May.
    3. 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.
    4. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    5. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    6. Haojie Mo & Yonggang Peng & Wei Wei & Wei Xi & Tiantian Cai, 2022. "SR-GNN Based Fault Classification and Location in Power Distribution Network," Energies, MDPI, vol. 16(1), pages 1-15, December.
    7. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    8. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    9. 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.
    10. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    11. 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.
    12. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    13. Jia, Ke & Li, Yanbin & Fang, Yu & Zheng, Liming & Bi, Tianshu & Yang, Qixun, 2018. "Transient current similarity based protection for wind farm transmission lines," Applied Energy, Elsevier, vol. 225(C), pages 42-51.
    14. Shazia Baloch & Saeed Zaman Jamali & Khawaja Khalid Mehmood & Syed Basit Ali Bukhari & Muhammad Saeed Uz Zaman & Arif Hussain & Chul-Hwan Kim, 2020. "Microgrid Protection Strategy Based on the Autocorrelation of Current Envelopes Using the Squaring and Low-Pass Filtering Method," Energies, MDPI, vol. 13(9), pages 1-13, 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. Mohd Zin, Abdullah Asuhaimi B. & Pesaran H.A., Mahmoud & Khairuddin, Azhar B. & Jahanshaloo, Leila & Shariati, Omid, 2013. "An overview on doubly fed induction generators′ controls and contributions to wind based electricity generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 692-708.
    17. Miguel Jiménez-Aparicio & Matthew J. Reno & Felipe Wilches-Bernal, 2022. "Traveling Wave Energy Analysis of Faults on Power Distribution Systems," Energies, MDPI, vol. 15(8), pages 1-28, April.
    18. 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).
    19. 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.
    20. 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.

    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:eee:appene:v:211:y:2018:i:c:p:568-581. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.