IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i10p6197-d819356.html
   My bibliography  Save this article

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

Author

Listed:
  • Kai Ding

    (State Grid Hubei Electric Power Company Limited Research Institute, Wuhan 430000, China)

  • Chen Yao

    (No. 2 Institute of Water Transportation, Anhui Transport Survey and Design Institute Co., Ltd., Hefei 230011, China)

  • Yifan Li

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430000, China)

  • Qinglong Hao

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430000, China)

  • Yaqiong Lv

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430000, China)

  • Zengrui Huang

    (State Grid Hubei Electric Power Company Limited Research Institute, Wuhan 430000, China)

Abstract

Nowadays, cold ironing technology has been demonstrated to be an effective solution to deal with the environmental and social problems brought by port ship emissions and relevant effects. The working states of cold ironing equipment, especially the key components such as circuit breakers, transformers and frequency converters, have a significant effect on the safety and reliability of the whole system. However, due to the harsh working environment of cold ironing equipment, they are prone to a high risk of failure. In this respect, fault diagnosis methods can play a significant role in detecting potential failure in time and guarantee the safe and reliable operation of the cold ironing system. In recent years, research on the fault diagnosis of a cold ironing system has been rapidly growing, and this paper aims to present a comprehensive review of this literature, with an emphasis on the fault diagnosis technology applied to the key components in a cold ironing system. This review classifies the literature according to the type of key component, and, for each special type of component, the fault diagnosis methods are further categorized and analyzed. This paper provides useful references for professionals and researchers working on the fault diagnosis of a cold ironing system and points out valuable research directions in the future.

Suggested Citation

  • Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6197-:d:819356
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/10/6197/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/10/6197/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao Luo & Zhiyuan Zhang & Xu Yan & Jinghui Qin & Zhendong Zhu & Hao Wang & Zeyong Gao, 2020. "Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, November.
    2. Daqian Wei & Bo Wang & Gang Lin & Dichen Liu & Zhaoyang Dong & Hesen Liu & Yilu Liu, 2017. "Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report," Energies, MDPI, vol. 10(3), pages 1-22, March.
    3. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine," Energies, MDPI, vol. 12(5), pages 1-18, March.
    4. Jose R. Huerta-Rosales & David Granados-Lieberman & Juan P. Amezquita-Sanchez & David Camarena-Martinez & Martin Valtierra-Rodriguez, 2020. "Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach," Mathematics, MDPI, vol. 8(4), pages 1-17, April.
    5. Haikun Shang & Junyan Xu & Zitao Zheng & Bing Qi & Liwei Zhang, 2019. "A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory," Energies, MDPI, vol. 12(20), pages 1-22, October.
    6. Jun Lin & Lei Su & Yingjie Yan & Gehao Sheng & Da Xie & Xiuchen Jiang, 2018. "Prediction Method for Power Transformer Running State Based on LSTM_DBN Network," Energies, MDPI, vol. 11(7), pages 1-14, July.
    7. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
    8. Yan Wang & Liguo Zhang, 2017. "A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-6, May.
    9. Chun Yan & Meixuan Li & Wei Liu, 2019. "Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, July.
    10. Jing Wu & Kun Li & Jing Sun & Li Xie, 2018. "A Novel Integrated Method to Diagnose Faults in Power Transformers," Energies, MDPI, vol. 11(11), pages 1-8, November.
    11. Carlos A. Reusser & Joel R. Pérez, 2020. "Evaluation of the Emission Impact of Cold-Ironing Power Systems, Using a Bi-Directional Power Flow Control Strategy," Sustainability, MDPI, vol. 13(1), pages 1-16, December.
    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. Saud Altaf & Shafiq Ahmad & Mazen Zaindin & Shamsul Huda & Sofia Iqbal & Muhammad Waseem Soomro, 2022. "Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network," Sustainability, MDPI, vol. 14(16), pages 1-29, August.
    2. Wang, Jinggai & Li, Huanhuan & Yang, Zaili & Ge, Ying-En, 2024. "Shore power for reduction of shipping emission in port: A bibliometric analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).

    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. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.
    2. Janvier Sylvestre N’cho & Issouf Fofana, 2020. "Review of Fiber Optic Diagnostic Techniques for Power Transformers," Energies, MDPI, vol. 13(7), pages 1-24, April.
    3. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
    4. Josue A. Reyes-Malanche & Francisco J. Villalobos-Pina & Efraın Ramırez-Velasco & Eduardo Cabal-Yepez & Geovanni Hernandez-Gomez & Misael Lopez-Ramirez, 2023. "Short-Circuit Fault Diagnosis on Induction Motors through Electric Current Phasor Analysis and Fuzzy Logic," Energies, MDPI, vol. 16(1), pages 1-15, January.
    5. Wei Zhang & Xiaohui Yang & Yeheng Deng & Anyi Li, 2020. "An Inspired Machine-Learning Algorithm with a Hybrid Whale Optimization for Power Transformer PHM," Energies, MDPI, vol. 13(12), pages 1-17, June.
    6. Hongchen Li & Zhong Yang & Jiaming Han & Shangxiang Lai & Qiuyan Zhang & Chi Zhang & Qianhui Fang & Guoxiong Hu, 2020. "TL-Net: A Novel Network for Transmission Line Scenes Classification," Energies, MDPI, vol. 13(15), pages 1-15, July.
    7. Fei Mei & Yong Ren & Qingliang Wu & Chenyu Zhang & Yi Pan & Haoyuan Sha & Jianyong Zheng, 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network," Energies, MDPI, vol. 12(1), pages 1-16, December.
    8. Hong Pan & Chenyang Hang & Fang Feng & Yuan Zheng & Fang Li, 2022. "Improved Neural Network Algorithm Based Flow Characteristic Curve Fitting for Hydraulic Turbines," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    9. Przemyslaw Pietrzak & Marcin Wolkiewicz, 2021. "Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents," Energies, MDPI, vol. 14(6), pages 1-23, March.
    10. Yongcong Luo & Jing Ma & Chi Li, 2020. "Entity name recognition of cross-border e-commerce commodity titles based on TWs-LSTM," Electronic Commerce Research, Springer, vol. 20(2), pages 405-426, June.
    11. Zhi-Jun Li & Wei-Gen Chen & Jie Shan & Zhi-Yong Yang & Ling-Yan Cao, 2022. "Enhanced Distributed Parallel Firefly Algorithm Based on the Taguchi Method for Transformer Fault Diagnosis," Energies, MDPI, vol. 15(9), pages 1-22, April.
    12. Maciej Skowron & Czeslaw T. Kowalski & Teresa Orlowska-Kowalska, 2022. "Impact of the Convolutional Neural Network Structure and Training Parameters on the Effectiveness of the Diagnostic Systems of Modern AC Motor Drives," Energies, MDPI, vol. 15(19), pages 1-22, September.
    13. Khaled Farag & Abdullah Shawier & Ayman S. Abdel-Khalik & Mohamed M. Ahmed & Shehab Ahmed, 2021. "Applicability Analysis of Indices-Based Fault Detection Technique of Six-Phase Induction Motor," Energies, MDPI, vol. 14(18), pages 1-23, September.
    14. Franciszek Witos & Aneta Olszewska, 2023. "Investigation of Partial Discharges within Power Oil Transformers by Acoustic Emission," Energies, MDPI, vol. 16(9), pages 1-20, April.
    15. Federico Gargiulo & Annalisa Liccardo & Rosario Schiano Lo Moriello, 2022. "A Non-Invasive Method Based on AI and Current Measurements for the Detection of Faults in Three-Phase Motors," Energies, MDPI, vol. 15(12), pages 1-19, June.
    16. Piotr Kołodziejek & Daniel Wachowiak, 2022. "Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive," Energies, MDPI, vol. 15(3), pages 1-14, February.
    17. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2020. "An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil," Energies, MDPI, vol. 13(7), pages 1-28, April.
    18. Roko Glavinović & Maja Krčum & Luka Vukić & Ivan Karin, 2023. "Cold Ironing Implementation Overview in European Ports—Case Study—Croatian Ports," Sustainability, MDPI, vol. 15(11), pages 1-18, May.
    19. Giovanni Betta & Domenico Capriglione & Luigi Ferrigno & Marco Laracca & Gianfranco Miele & Nello Polese & Silvia Sangiovanni, 2021. "A Fault Diagnostic Scheme for Predictive Maintenance of AC/DC Converters in MV/LV Substations," Energies, MDPI, vol. 14(22), pages 1-23, November.
    20. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.

    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:jsusta:v:14:y:2022:i:10:p:6197-:d:819356. 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.