IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i12p3106-d1677720.html
   My bibliography  Save this article

Advances in HVDC Systems: Aspects, Principles, and a Comprehensive Review of Signal Processing Techniques for Fault Detection

Author

Listed:
  • Leyla Zafari

    (Department of Electrical, Computer, and Software Engineering, University of Auckland, Auckland 1010, New Zealand)

  • Yuan Liu

    (Department of Electrical, Computer, and Software Engineering, University of Auckland, Auckland 1010, New Zealand)

  • Abhisek Ukil

    (Department of Electrical, Computer, and Software Engineering, University of Auckland, Auckland 1010, New Zealand)

  • Nirmal-Kumar C. Nair

    (Department of Electrical, Computer, and Software Engineering, University of Auckland, Auckland 1010, New Zealand)

Abstract

This paper presents a comprehensive review of High-Voltage Direct-Current (HVDC) systems, focusing on their technological evolution, fault characteristics, and advanced signal processing techniques for fault detection. The paper traces the development of HVDC links globally, highlighting the transition from mercury-arc valves to Insulated Gate Bipolar Transistor (IGBT)-based converters and showcasing operational projects in technologically advanced countries. A detailed comparison of converter technologies including line-commutated converters (LCCs), Voltage-Source Converters (VSCs), and Modular Multilevel Converters (MMCs) and pole configurations (monopolar, bipolar, homopolar, and MMC) is provided. The paper categorizes HVDC faults into AC, converter, and DC types, focusing on their primary locations and fault characteristics. Signal processing methods, including time-domain, frequency-domain, and time–frequency-domain approaches, are systematically compared, supported by relevant case studies. The review identifies critical research gaps in enhancing the reliability of fault detection, classification, and protection under diverse fault conditions, offering insights into future advancements in HVDC system resilience.

Suggested Citation

  • Leyla Zafari & Yuan Liu & Abhisek Ukil & Nirmal-Kumar C. Nair, 2025. "Advances in HVDC Systems: Aspects, Principles, and a Comprehensive Review of Signal Processing Techniques for Fault Detection," Energies, MDPI, vol. 18(12), pages 1-37, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3106-:d:1677720
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/12/3106/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/12/3106/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Umar Javed & Neelam Mughees & Muhammad Jawad & Omar Azeem & Ghulam Abbas & Nasim Ullah & Md. Shahariar Chowdhury & Kuaanan Techato & Khurram Shabih Zaidi & Umair Tahir, 2021. "A Systematic Review of Key Challenges in Hybrid HVAC–HVDC Grids," Energies, MDPI, vol. 14(17), pages 1-27, September.
    2. Tarek Abedin & M. Shahadat Hossain Lipu & Mahammad A. Hannan & Pin Jern Ker & Safwan A. Rahman & Chong Tak Yaw & Sieh K. Tiong & Kashem M. Muttaqi, 2021. "Dynamic Modeling of HVDC for Power System Stability Assessment: A Review, Issues, and Recommendations," Energies, MDPI, vol. 14(16), pages 1-25, August.
    3. Raad Salih Jawad & Hafedh Abid, 2023. "HVDC Fault Detection and Classification with Artificial Neural Network Based on ACO-DWT Method," Energies, MDPI, vol. 16(3), pages 1-18, January.
    4. Shuhao Liu & Kunlun Han & Hongzheng Li & Tengyue Zhang & Fengyuan Chen, 2023. "A Two-Terminal Directional Protection Method for HVDC Transmission Lines of Current Fault Component Based on Improved VMD-Hilbert Transform," Energies, MDPI, vol. 16(19), pages 1-21, October.
    5. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan K., 2021. "MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid," Applied Energy, Elsevier, vol. 285(C).
    6. Yasser Damine & Noureddine Bessous & Remus Pusca & Ahmed Chaouki Megherbi & Raphaël Romary & Salim Sbaa, 2023. "A New Bearing Fault Detection Strategy Based on Combined Modes Ensemble Empirical Mode Decomposition, KMAD, and an Enhanced Deconvolution Process," Energies, MDPI, vol. 16(6), pages 1-27, March.
    7. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    8. Gul Ahmad Ludin & Akito Nakadomari & Atsushi Yona & Suresh Mikkili & Shriram Srinivasarangan Rangarajan & Edward Randolph Collins & Tomonobu Senjyu, 2022. "Technical and Economic Analysis of an HVDC Transmission System for Renewable Energy Connection in Afghanistan," Sustainability, MDPI, vol. 14(3), pages 1-19, January.
    9. Qian Chen & Qiang Li & Jiyang Wu & Jingsong He & Chizu Mao & Ziyou Li & Bo Yang, 2023. "State Monitoring and Fault Diagnosis of HVDC System via KNN Algorithm with Knowledge Graph: A Practical China Power Grid Case," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    10. Jashandeep Kaur & Manilka Jayasooriya & Muhammad Naveed Iqbal & Kamran Daniel & Noman Shabbir & Kristjan Peterson, 2025. "Fault Detection and Protection Strategy for Multi-Terminal HVDC Grids Using Wavelet Analysis," Energies, MDPI, vol. 18(5), pages 1-19, February.
    11. Kyu-hoon Park & Il Kwon & Bang-wook Lee, 2021. "Calculation Method of Allowable Continuous Current for Direct Burial Laying HVDC Underground Cable," Energies, MDPI, vol. 14(19), pages 1-16, October.
    12. Giovanni Mazzanti, 2021. "Issues and Challenges for HVDC Extruded Cable Systems," Energies, MDPI, vol. 14(15), pages 1-34, July.
    13. Raad Salih Jawad & Hafedh Abid, 2022. "Fault Detection in HVDC System with Gray Wolf Optimization Algorithm Based on Artificial Neural Network," Energies, MDPI, vol. 15(20), pages 1-17, October.
    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. Abha Pragati & Manohar Mishra & Pravat Kumar Rout & Debadatta Amaresh Gadanayak & Shazia Hasan & B. Rajanarayan Prusty, 2023. "A Comprehensive Survey of HVDC Protection System: Fault Analysis, Methodology, Issues, Challenges, and Future Perspective," Energies, MDPI, vol. 16(11), pages 1-39, May.
    2. Alam, Md Morshed & Hossain, M.J. & Habib, Md Ahasan & Arafat, M.Y. & Hannan, M.A., 2025. "Artificial intelligence integrated grid systems: Technologies, potential frameworks, challenges, and research directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 211(C).
    3. Jianguo Zhang & Pengcheng Sha & Feiyue Ma & Bo Niu & Xu Cai & Hui Ni & Junbo Deng, 2025. "Research on Opening Reignition Characteristics and Suppression Measures of 750 kV AC Filter Circuit Breakers," Energies, MDPI, vol. 18(1), pages 1-18, January.
    4. Tomasz Szczegielniak & Paweł Jabłoński & Dariusz Kusiak, 2023. "Analytical Approach to Current Rating of Three-Phase Power Cable with Round Conductors," Energies, MDPI, vol. 16(4), pages 1-18, February.
    5. Yanghe Liu & Hairong Zhang & Chuanfeng Wu & Mengxin Shao & Liting Zhou & Wenlong Fu, 2024. "A Short-Term Wind Speed Forecasting Framework Coupling a Maximum Information Coefficient, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Shared Weight Gated Memory Network with Im," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    6. Cen, Xiao & Chen, Zengliang & Chen, Haifeng & Ding, Chen & Ding, Bo & Li, Fei & Lou, Fangwei & Zhu, Zhenyu & Zhang, Hongyu & Hong, Bingyuan, 2024. "User repurchase behavior prediction for integrated energy supply stations based on the user profiling method," Energy, Elsevier, vol. 286(C).
    7. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    8. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    9. Gao, Jiaxin & Cheng, Yuanqi & Zhang, Dongxiao & Chen, Yuntian, 2025. "Physics-constrained wind power forecasting aligned with probability distributions for noise-resilient deep learning," Applied Energy, Elsevier, vol. 383(C).
    10. Kim, Daeyoung & Ryu, Geonhwa & Moon, Chaejoo & Kim, Bumsuk, 2024. "Accuracy of a short-term wind power forecasting model based on deep learning using LiDAR-SCADA integration: A case study of the 400-MW Anholt offshore wind farm," Applied Energy, Elsevier, vol. 373(C).
    11. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    12. Sahebkar Farkhani, Jalal & Çelik, Özgür & Ma, Kaiqi & Bak, Claus Leth & Chen, Zhe, 2024. "A comprehensive review of potential protection methods for VSC multi-terminal HVDC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    13. Xu, Jing & Cui, Zhipeng & Ma, Suxia & Wang, Xiaowei & Zhang, Zhiyao & Zhang, Guoxia, 2024. "Data based digital twin for operational performance optimization in CFB boilers," Energy, Elsevier, vol. 306(C).
    14. Zhou, Hanmi & Ma, Linshuang & Niu, Xiaoli & Xiang, Youzhen & Chen, Jiageng & Su, Yumin & Li, Jichen & Lu, Sibo & Chen, Cheng & Wu, Qi, 2024. "A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain," Agricultural Water Management, Elsevier, vol. 296(C).
    15. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
    16. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    17. Mahfoud Bouzouidja & Moncef Soualhi & Abdenour Soualhi & Hubert Razik, 2024. "Detection and Diagnostics of Bearing and Gear Fault under Variable Speed and Load Conditions Using Heterogeneous Signals," Energies, MDPI, vol. 17(3), pages 1-25, January.
    18. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
    19. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    20. Jiang, Wenjun & Liu, Bo & Liang, Yang & Gao, Huanxiang & Lin, Pengfei & Zhang, Dongqin & Hu, Gang, 2024. "Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables," Applied Energy, Elsevier, vol. 353(PB).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:12:p:3106-:d:1677720. 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.