IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3494-d381071.html
   My bibliography  Save this article

Distributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-Healing

Author

Listed:
  • Miftah Al Karim

    (The Lines Company, Te Kuiti 3910, New Zealand)

  • Jonathan Currie

    (Rocket Lab, Auckland 1060, New Zealand)

  • Tek-Tjing Lie

    (School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand)

Abstract

Numerous online methods for post-fault restoration have been tested on different types of systems. Modern power systems are usually operated at design limits and therefore more prone to post-fault instability. However, traditional online methods often struggle to accurately identify events from time series data, as pattern-recognition in a stochastic post-fault dynamic scenario requires fast and accurate fault identification in order to safely restore the system. One of the most prominent methods of pattern-recognition is machine learning. However, machine learning alone is neither sufficient nor accurate enough for making decisions with time series data. This article analyses the application of feature selection to assist a machine learning algorithm to make better decisions in order to restore a multi-machine network which has become islanded due to faults. Within an islanded multi-machine system the number of attributes significantly increases, which makes application of machine learning algorithms even more erroneous. This article contributes by proposing a distributed offline-online architecture. The proposal explores the potential of introducing relevant features from a reduced time series data set, in order to accurately identify dynamic events occurring in different islands simultaneously. The identification of events helps the decision making process more accurate.

Suggested Citation

  • Miftah Al Karim & Jonathan Currie & Tek-Tjing Lie, 2020. "Distributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-Healing," Energies, MDPI, vol. 13(13), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3494-:d:381071
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3494/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/13/3494/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jurado, Sergio & Nebot, Àngela & Mugica, Fransisco & Avellana, Narcís, 2015. "Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques," Energy, Elsevier, vol. 86(C), pages 276-291.
    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. Nastaran Gholizadeh & Petr Musilek, 2021. "Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges," Energies, MDPI, vol. 14(12), pages 1-18, June.
    2. Tek-Tjing Lie, 2021. "Editorial to the Special Issue “AI Applications to Power Systems”," Energies, MDPI, vol. 14(18), pages 1-3, September.

    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. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    2. Yang, Yang & Xue, Dingyü, 2016. "Continuous fractional-order grey model and electricity prediction research based on the observation error feedback," Energy, Elsevier, vol. 115(P1), pages 722-733.
    3. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    4. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    5. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    6. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
    7. Bilin Shao & Yichuan Yan & Huibin Zeng, 2022. "VMD-WSLSTM Load Prediction Model Based on Shapley Values," Energies, MDPI, vol. 15(2), pages 1-18, January.
    8. Sungwoo Park & Jihoon Moon & Seungwon Jung & Seungmin Rho & Sung Wook Baik & Eenjun Hwang, 2020. "A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling," Energies, MDPI, vol. 13(2), pages 1-23, January.
    9. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    10. Jovani Taveira de Souza & Antonio Carlos de Francisco & Cassiano Moro Piekarski & Guilherme Francisco do Prado, 2019. "Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018," Sustainability, MDPI, vol. 11(4), pages 1-14, February.
    11. Bijay Neupane & Wei Lee Woon & Zeyar Aung, 2017. "Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting," Energies, MDPI, vol. 10(1), pages 1-27, January.
    12. João Vitor Leme & Wallace Casaca & Marilaine Colnago & Maurício Araújo Dias, 2020. "Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models," Energies, MDPI, vol. 13(6), pages 1-20, March.
    13. Nowotarski, Jakub & Liu, Bidong & Weron, Rafał & Hong, Tao, 2016. "Improving short term load forecast accuracy via combining sister forecasts," Energy, Elsevier, vol. 98(C), pages 40-49.
    14. Marcelo Moya & Javier Martínez-Gómez & Esteban Urresta & Martín Cordovez-Dammer, 2022. "Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island," Energies, MDPI, vol. 15(8), pages 1-17, April.
    15. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    16. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
    17. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
    18. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
    19. He, Deqiang & Liu, Chenyu & Jin, Zhenzhen & Ma, Rui & Chen, Yanjun & Shan, Sheng, 2022. "Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning," Energy, Elsevier, vol. 239(PB).
    20. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(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:gam:jeners:v:13:y:2020:i:13:p:3494-:d:381071. 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.