IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v191y2024ics1364032123009528.html
   My bibliography  Save this article

Power quality monitoring in electric grid integrating offshore wind energy: A review

Author

Listed:
  • Shao, Han
  • Henriques, Rui
  • Morais, Hugo
  • Tedeschi, Elisabetta

Abstract

The rising integration of offshore wind energy into the electric grid provides remarkable opportunities in terms of environmental sustainability and cost efficiency. However, it poses challenges to power quality (PQ) caused by variable operational events and power electronic devices used in wind turbines. This renders early-stage disturbance detection and classification tools critical for managing grid systems with renewable energy sources. While existing reviews specialize in the monitoring measures of partial PQ events, this survey offers a deeper understanding by further exploring root causes and disturbance locations, followed by a critical discussion of emerging algorithmic solutions. In contrast with generalists’ stances on PQ, this work delves into the impact of offshore wind energy on PQ events and their early diagnosis. Moreover, the principles and applications of synchronized waveform measurement, a promising measurement technology for detection and classification processes, are highlighted. Then, evaluation metrics for detection and classification algorithms are discussed for the first time. Finally, a novel system-wide monitoring framework is proposed given the need for holistic assessment frames in this field. This review not only illustrates the challenges and future research directions in the level of algorithms, measurements, and frameworks, but can also serve as a guideline for real-time disturbance analysis of offshore wind power grid connection and integration.

Suggested Citation

  • Shao, Han & Henriques, Rui & Morais, Hugo & Tedeschi, Elisabetta, 2024. "Power quality monitoring in electric grid integrating offshore wind energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
  • Handle: RePEc:eee:rensus:v:191:y:2024:i:c:s1364032123009528
    DOI: 10.1016/j.rser.2023.114094
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123009528
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2023.114094?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. Mahela, Om Prakash & Shaik, Abdul Gafoor & Gupta, Neeraj, 2015. "A critical review of detection and classification of power quality events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 495-505.
    2. Chen, Lei & Xie, Xiaorong & He, Jingbo & Xu, Tao & Xu, Dechao & Ma, Ningning, 2023. "Wideband oscillation monitoring in power systems with high-penetration of renewable energy sources and power electronics: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    3. Joselin Herbert, G.M. & Iniyan, S. & Sreevalsan, E. & Rajapandian, S., 2007. "A review of wind energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(6), pages 1117-1145, August.
    4. Robles, Eider & Haro-Larrode, Marta & Santos-Mugica, Maider & Etxegarai, Agurtzane & Tedeschi, Elisabetta, 2019. "Comparative analysis of European grid codes relevant to offshore renewable energy installations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 171-185.
    5. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    6. Yue Shen & Muhammad Abubakar & Hui Liu & Fida Hussain, 2019. "Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems," Energies, MDPI, vol. 12(7), pages 1-26, April.
    7. Khokhar, Suhail & Mohd Zin, Abdullah Asuhaimi B. & Mokhtar, Ahmad Safawi B. & Pesaran, Mahmoud, 2015. "A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1650-1663.
    8. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    9. Julio Barros & Matilde de Apráiz & Ramón I. Diego, 2019. "Power Quality in DC Distribution Networks," Energies, MDPI, vol. 12(5), pages 1-13, March.
    10. Kang, Jichuan & Sun, Liping & Guedes Soares, C., 2019. "Fault Tree Analysis of floating offshore wind turbines," Renewable Energy, Elsevier, vol. 133(C), pages 1455-1467.
    11. Shair, Jan & Li, Haozhi & Hu, Jiabing & Xie, Xiaorong, 2021. "Power system stability issues, classifications and research prospects in the context of high-penetration of renewables and power electronics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    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. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Zakarya Oubrahim & Yassine Amirat & Mohamed Benbouzid & Mohammed Ouassaid, 2023. "Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review," Energies, MDPI, vol. 16(6), pages 1-41, March.
    3. Zhang, Liangheng & Jiang, Congmei & Pang, Aiping & He, Yu, 2024. "Super-efficient detector and defense method for adversarial attacks in power quality classification," Applied Energy, Elsevier, vol. 361(C).
    4. Paula Remigio-Carmona & Juan-José González-de-la-Rosa & Olivia Florencias-Oliveros & José-María Sierra-Fernández & Javier Fernández-Morales & Manuel-Jesús Espinosa-Gavira & Agustín Agüera-Pérez & José, 2022. "Current Status and Future Trends of Power Quality Analysis," Energies, MDPI, vol. 15(7), pages 1-18, March.
    5. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
    6. Juan-José González de-la-Rosa & Manuel Pérez-Donsión, 2020. "Special Issue “Analysis for Power Quality Monitoring”," Energies, MDPI, vol. 13(3), pages 1-6, January.
    7. Artvin-Darien Gonzalez-Abreu & Miguel Delgado-Prieto & Roque-Alfredo Osornio-Rios & Juan-Jose Saucedo-Dorantes & Rene-de-Jesus Romero-Troncoso, 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances," Energies, MDPI, vol. 14(10), pages 1-17, May.
    8. Samet, Haidar, 2016. "Evaluation of digital metering methods used in protection and reactive power compensation of micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 260-279.
    9. David Lumbreras & Eduardo Gálvez & Alfonso Collado & Jordi Zaragoza, 2020. "Trends in Power Quality, Harmonic Mitigation and Standards for Light and Heavy Industries: A Review," Energies, MDPI, vol. 13(21), pages 1-24, November.
    10. Artvin-Darien Gonzalez-Abreu & Roque-Alfredo Osornio-Rios & Arturo-Yosimar Jaen-Cuellar & Miguel Delgado-Prieto & Jose-Alfonso Antonino-Daviu & Athanasios Karlis, 2022. "Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review," Energies, MDPI, vol. 15(5), pages 1-26, March.
    11. Misael Lopez-Ramirez & Luis Ledesma-Carrillo & Eduardo Cabal-Yepez & Carlos Rodriguez-Donate & Homero Miranda-Vidales & Arturo Garcia-Perez, 2016. "EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments," Energies, MDPI, vol. 9(7), pages 1-15, July.
    12. Eslami, Ahmadreza & Negnevitsky, Michael & Franklin, Evan & Lyden, Sarah, 2022. "Review of AI applications in harmonic analysis in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    13. Cheng-I Chen & Sunneng Sandino Berutu & Yeong-Chin Chen & Hao-Cheng Yang & Chung-Hsien Chen, 2022. "Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid," Energies, MDPI, vol. 15(7), pages 1-16, March.
    14. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    15. Radovan Turović & Dinu Dragan & Gorana Gojić & Veljko B. Petrović & Dušan B. Gajić & Aleksandar M. Stanisavljević & Vladimir A. Katić, 2022. "An End-to-End Deep Learning Method for Voltage Sag Classification," Energies, MDPI, vol. 15(8), pages 1-22, April.
    16. Oluwafemi Emmanuel Oni & Omowunmi Mary Longe, 2023. "Analysis of Secondary Controller on MTDC Link with Solar PV Integration for Inter-Area Power Oscillation Damping," Energies, MDPI, vol. 16(17), pages 1-18, August.
    17. Nantian Huang & Hua Peng & Guowei Cai & Jikai Chen, 2016. "Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm," Energies, MDPI, vol. 9(11), pages 1-21, November.
    18. Vojtech Blazek & Michal Petruzela & Tomas Vantuch & Zdenek Slanina & Stanislav Mišák & Wojciech Walendziuk, 2020. "The Estimation of the Influence of Household Appliances on the Power Quality in a Microgrid System," Energies, MDPI, vol. 13(17), pages 1-21, August.
    19. Azcarate, I. & Gutierrez, J.J. & Lazkano, A. & Saiz, P. & Redondo, K. & Leturiondo, L.A., 2016. "Towards limiting the sensitivity of energy-efficient lighting to voltage fluctuations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1384-1395.
    20. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).

    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:rensus:v:191:y:2024:i:c:s1364032123009528. 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/600126/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.