IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i16p5972-d1216849.html
   My bibliography  Save this article

An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks

Author

Listed:
  • Sepideh Radhoush

    (Electrical and Computer Engineering Department, Montana State University, Bozeman, MT 59717, USA)

  • Bradley M. Whitaker

    (Electrical and Computer Engineering Department, Montana State University, Bozeman, MT 59717, USA)

  • Hashem Nehrir

    (Electrical and Computer Engineering Department, Montana State University, Bozeman, MT 59717, USA)

Abstract

Distribution grids must be regularly updated to meet the global electricity demand. Some of these updates result in fundamental changes to the structure of the grid network. Some recent changes include two-way communication infrastructure, the rapid development of distributed generations (DGs) in different forms, and the installation of smart measurement tools. In addition to other changes, these lead to distribution grid modifications, allowing more advanced features. Even though these advanced technologies enhance distribution grid performance, the operation, management, and control of active distribution networks (ADNs) have become more complicated. For example, distribution system state estimation (DSSE) calculations have been introduced as a tool to estimate the performance of distribution grids. These DSSE computations are highly dependent on data obtained from measurement devices in distribution grids. However, sufficient measurement devices are not available in ADNs due to economic constraints and various configurations of distribution grids. Thus, the modeling of pseudo-measurements using conventional and machine learning techniques from historical information in distribution grids is applied to address the lack of real measurements in ADNs. Different types of measurements (real, pseudo, and virtual measurements), alongside network parameters, are fed into model-based or data-based DSSE approaches to estimate the state variables of the distribution grid. The results obtained through DSSE should be sufficiently accurate for the appropriate management and overall performance evaluation of a distribution grid in a control center. However, distribution grids are prone to different cyberattacks, which can endanger their safe operation. One particular type of cyberattack is known as a false data injection attack (FDIA) on measurement data. Attackers try to inject false data into the measurements of nodes to falsify DSSE results. The FDIA can sometimes bypass poor traditional data-detection processes. If FDIAs cannot be identified successfully, the distribution grid’s performance is degraded significantly. Currently, different machine learning applications are applied widely to model pseudo-measurements, calculate DSSE variables, and identify FDIAs on measurement data to achieve the desired distribution grid operation and performance. In this study, we present a comprehensive review investigating the use of supervised machine learning (SML) in distribution grids to enhance and improve the operation and performance of advanced distribution grids according to three perspectives: (1) pseudo-measurement generation (via short-term load forecasting); (2) DSSE calculation; and (3) FDIA detection on measurement data. This review demonstrates the importance of SML in the management of ADN operation.

Suggested Citation

  • Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5972-:d:1216849
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/16/5972/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/16/5972/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sekhar, Charan & Dahiya, Ratna, 2023. "Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand," Energy, Elsevier, vol. 268(C).
    2. Vidya Krishnan Mololoth & Saguna Saguna & Christer Åhlund, 2023. "Blockchain and Machine Learning for Future Smart Grids: A Review," Energies, MDPI, vol. 16(1), pages 1-39, January.
    3. Mengjia He & Yingnian Wu & Tao Wang & Yujie Chen, 2021. "Research on dry electrode SSVEP classification algorithm based on improved convolutional neural network," International Journal of Service and Computing Oriented Manufacturing, Inderscience Enterprises Ltd, vol. 4(1), pages 70-88.
    4. Junjun Xu & Yulong Jin & Tao Zheng & Gaojun Meng, 2023. "On State Estimation Modeling of Smart Distribution Networks: A Technical Review," Energies, MDPI, vol. 16(4), pages 1-19, February.
    5. Ahmad, Fiaz & Rasool, Akhtar & Ozsoy, Emre & Sekar, Raja & Sabanovic, Asif & Elitaş, Meltem, 2018. "Distribution system state estimation-A step towards smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2659-2671.
    6. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    7. Huiping Jiang & Demeng Wu & Rui Jiao & Zongnan Wang & Ning Cai, 2021. "Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks," Complexity, Hindawi, vol. 2021, pages 1-9, February.
    8. Karthikeyan Nainar & Florin Iov, 2020. "Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids," Energies, MDPI, vol. 13(20), pages 1-18, October.
    9. Aghahosseini, Arman & Solomon, A.A. & Breyer, Christian & Pregger, Thomas & Simon, Sonja & Strachan, Peter & Jäger-Waldau, Arnulf, 2023. "Energy system transition pathways to meet the global electricity demand for ambitious climate targets and cost competitiveness," Applied Energy, Elsevier, vol. 331(C).
    10. Junhwa Hwang & Dongjun Suh & Marc-Oliver Otto, 2020. "Forecasting Electricity Consumption in Commercial Buildings Using a Machine Learning Approach," Energies, MDPI, vol. 13(22), pages 1-29, November.
    11. Bruce Stephen, 2022. "Machine Learning Applications in Power System Condition Monitoring," Energies, MDPI, vol. 15(5), pages 1-2, March.
    12. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    13. Ashfaq Ahmad & Nadeem Javaid & Abdul Mateen & Muhammad Awais & Zahoor Ali Khan, 2019. "Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach," Energies, MDPI, vol. 12(1), pages 1-21, January.
    14. 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).
    15. Vinoth Kumar Ponnusamy & Padmanathan Kasinathan & Rajvikram Madurai Elavarasan & Vinoth Ramanathan & Ranjith Kumar Anandan & Umashankar Subramaniam & Aritra Ghosh & Eklas Hossain, 2021. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid," Sustainability, MDPI, vol. 13(23), pages 1-35, December.
    16. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    17. Basanta Raj Pokhrel & Birgitte Bak-Jensen & Jayakrishnan R. Pillai, 2019. "Integrated Approach for Network Observability and State Estimation in Active Distribution Grid," Energies, MDPI, vol. 12(12), pages 1-17, June.
    18. Pengwei Su & Xue Tian & Yan Wang & Shuai Deng & Jun Zhao & Qingsong An & Yongzhen Wang, 2017. "Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System," Energies, MDPI, vol. 10(9), pages 1-13, August.
    19. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    20. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    21. Antonio E. Saldaña-González & Andreas Sumper & Mònica Aragüés-Peñalba & Miha Smolnikar, 2020. "Advanced Distribution Measurement Technologies and Data Applications for Smart Grids: A Review," Energies, MDPI, vol. 13(14), pages 1-34, July.
    22. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
    23. Niknam, Taher & Firouzi, Bahman Bahmani, 2009. "A practical algorithm for distribution state estimation including renewable energy sources," Renewable Energy, Elsevier, vol. 34(11), pages 2309-2316.
    24. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    25. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    26. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    27. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    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. Aleksandr Gevorgian & Giovanni Pernigotto & Andrea Gasparella, 2024. "Addressing Data Scarcity in Solar Energy Prediction with Machine Learning and Augmentation Techniques," Energies, MDPI, vol. 17(14), pages 1-19, July.

    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. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    2. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    3. Yao, Haowei & Qu, Pengyu & Qin, Hengjie & Lou, Zhen & Wei, Xiaoge & Song, Huaitao, 2024. "Multidimensional electric power parameter time series forecasting and anomaly fluctuation analysis based on the AFFC-GLDA-RL method," Energy, Elsevier, vol. 313(C).
    4. Lei Zhang & Yuxing Yuan & Su Yan & Hang Cao & Tao Du, 2025. "Advances in Modeling and Optimization of Intelligent Power Systems Integrating Renewable Energy in the Industrial Sector: A Multi-Perspective Review," Energies, MDPI, vol. 18(10), pages 1-50, May.
    5. István Táczi & Bálint Sinkovics & István Vokony & Bálint Hartmann, 2021. "The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review," Energies, MDPI, vol. 14(17), pages 1-17, August.
    6. Aqdas Naz & Nadeem Javaid & Muhammad Babar Rasheed & Abdul Haseeb & Musaed Alhussein & Khursheed Aurangzeb, 2019. "Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid," Sustainability, MDPI, vol. 11(10), pages 1-22, May.
    7. Fabio Napolitano & Juan Diego Rios Penaloza & Fabio Tossani & Alberto Borghetti & Carlo Alberto Nucci, 2021. "Three-Phase State Estimation of a Low-Voltage Distribution Network with Kalman Filter," Energies, MDPI, vol. 14(21), pages 1-19, November.
    8. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
    9. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    10. Sepideh Radhoush & Trevor Vannoy & Kaveen Liyanage & Bradley M. Whitaker & Hashem Nehrir, 2023. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
    11. Cheng Qian & Xing Liu & Colin Ripley & Mian Qian & Fan Liang & Wei Yu, 2022. "Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions," Future Internet, MDPI, vol. 14(2), pages 1-25, February.
    12. Bianca Magalhães & Pedro Bento & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2024. "Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection," Energies, MDPI, vol. 17(8), pages 1-21, April.
    13. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    14. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    15. Umme Mumtahina & Sanath Alahakoon & Peter Wolfs, 2024. "Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review," Mathematics, MDPI, vol. 12(21), pages 1-51, October.
    16. Fangzong Wang & Zuhaib Nishter, 2024. "Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach," Energies, MDPI, vol. 17(11), pages 1-24, May.
    17. Hanshan Qing & Abhinav Kumar Singh & Efstratios Batzelis, 2025. "Review on Distribution System State Estimation Considering Renewable Energy Sources," Energies, MDPI, vol. 18(10), pages 1-20, May.
    18. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu, 2020. "Minutely Active Power Forecasting Models Using Neural Networks," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    19. Syed Saqib Ali & Mazhar Ali & Dost Muhammad Saqib Bhatti & Bong Jun Choi, 2025. "Explainable Clustered Federated Learning for Solar Energy Forecasting," Energies, MDPI, vol. 18(9), pages 1-19, May.
    20. Karthikeyan Nainar & Florin Iov, 2021. "Three-Phase State Estimation for Distribution-Grid Analytics," Clean Technol., MDPI, vol. 3(2), pages 1-14, May.

    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:16:y:2023:i:16:p:5972-:d:1216849. 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.