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A new fault detection and fault location method for multi-terminal high voltage direct current of offshore wind farm

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  • Li, Jianwei
  • Yang, Qingqing
  • Mu, Hao
  • Le Blond, Simon
  • He, Hongwen

Abstract

This paper proposes a novel protection scheme for multi-terminal High Voltage Direct Current (MTDC) systems incorporating offshore wind farm based on high-frequency components detected from the fault current signal. This method can accurately detect the fault on each line and classify the fault types. Using the post-fault current time series, both single-ended measurements (detection and classification) and double-end measurements (location), the frequency spectrum is generated to measure the gaps between the contiguous peak frequencies giving a robust and comprehensive scheme. Unlike the previous travelling wave based methods, which must identify the travelling wavefront and require a high sampling rate, the new gap-based approach is able to give accurate fault detection and fault location using any appropriate range of post-fault signals. Furthermore, the proposed method is fault resistance independent and thus even a very high fault impedance has no effect on the fault location detection. By immediately tripping the faults, the fault-caused disturbance to the offshore wind farm is minimized. A three-terminal voltage sourced converter HVDC (VSC-HVDC) system connection of offshore wind farm is modelled in PSCAD/EMTDC (Power Systems Computer Aided Design/Electro-Magnetic Transients including DC) software, which is used for obtaining the fault current data for the transmission line terminal. The algorithm is verified by studying a range of cases, by varying the fault resistance fault locations and also including external faults. The results show that the proposed method gives an accurate and reliable fault detection, classification and location on the test MTDC system.

Suggested Citation

  • Li, Jianwei & Yang, Qingqing & Mu, Hao & Le Blond, Simon & He, Hongwen, 2018. "A new fault detection and fault location method for multi-terminal high voltage direct current of offshore wind farm," Applied Energy, Elsevier, vol. 220(C), pages 13-20.
    • Handle: RePEc:eee:appene:v:220:y:2018:i:c:p:13-20
    DOI: 10.1016/j.apenergy.2018.03.044
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    3. Liang, Jinping & Zhang, Ke & Al-Durra, Ahmed & Zhou, Daming, 2020. "A novel fault diagnostic method in power converters for wind power generation system," Applied Energy, Elsevier, vol. 266(C).
    4. Terriche, Yacine & Lashab, Abderezak & Çimen, Halil & Guerrero, Josep M. & Su, Chun-Lien & Vasquez, Juan C., 2022. "Power quality assessment using signal periodicity independent algorithms – A shipboard microgrid case study," Applied Energy, Elsevier, vol. 307(C).
    5. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    6. Navid Bayati & Mehdi Savaghebi, 2021. "Protection Systems for DC Shipboard Microgrids," Energies, MDPI, vol. 14(17), pages 1-20, August.
    7. Qu, Fuming & Liu, Jinhai & Zhu, Hongfei & Zhou, Bowen, 2020. "Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic," Applied Energy, Elsevier, vol. 262(C).
    8. Perez-Molina, M.J. & Larruskain, D.M. & Eguia Lopez, P. & Buigues, G. & Valverde, V., 2021. "Review of protection systems for multi-terminal high voltage direct current grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    9. Yang, Qingqing & Li, Jianwei & Cao, Wanke & Li, Shuangqi & Lin, Jie & Huo, Da & He, Hongwen, 2020. "An improved vehicle to the grid method with battery longevity management in a microgrid application," Energy, Elsevier, vol. 198(C).
    10. 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).
    11. 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).
    12. Nguyen, Thi Anh Tuyet & Chou, Shuo-Yan, 2019. "Improved maintenance optimization of offshore wind systems considering effects of government subsidies, lost production and discounted cost model," Energy, Elsevier, vol. 187(C).

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