IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i18p6641-d912210.html
   My bibliography  Save this article

Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid

Author

Listed:
  • Yunus Yalman

    (Department of Electrical and Electronic Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey)

  • Tayfun Uyanık

    (Maritime Faculty, Istanbul Technical University, Istanbul 34940, Turkey)

  • İbrahim Atlı

    (Department of Computer Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey)

  • Adnan Tan

    (Department of Electrical and Electronics Engineering, Çukurova University, Adana 01250, Turkey)

  • Kamil Çağatay Bayındır

    (Department of Electrical and Electronic Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey)

  • Ömer Karal

    (Department of Electrical and Electronic Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey)

  • Saeed Golestan

    (Center for Research on Microgrids, AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Josep M. Guerrero

    (Center for Research on Microgrids, AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

Abstract

Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach to determine the voltage sag relative location based on voltage sag event records of the power quality monitoring system (PQMS) in the real distribution system. By this method, the relative location of voltage sag is defined by Gaussian naive Bayes (Gaussian NB) and K-nearest neighbors (K-NN) algorithms. The proposed methods are compared with support vector machine (SVM) and artificial neural network (ANN). The results indicate that K-NN and Gaussian NB algorithms define the relative location of a voltage sag with 98.75% and 97.34% accuracy, respectively.

Suggested Citation

  • Yunus Yalman & Tayfun Uyanık & İbrahim Atlı & Adnan Tan & Kamil Çağatay Bayındır & Ömer Karal & Saeed Golestan & Josep M. Guerrero, 2022. "Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid," Energies, MDPI, vol. 15(18), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6641-:d:912210
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/18/6641/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/18/6641/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamdy M. Sultan & Ahmed A. Zaki Diab & Oleg N. Kuznetsov & Ziad M. Ali & Omer Abdalla, 2019. "Evaluation of the Impact of High Penetration Levels of PV Power Plants on the Capacity, Frequency and Voltage Stability of Egypt’s Unified Grid," Energies, MDPI, vol. 12(3), pages 1-22, February.
    2. Seungbeom Nam & Jin Hur, 2018. "Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models," Energies, MDPI, vol. 11(11), pages 1-15, November.
    3. Maen Z. Kreishan & George P. Fotis & Vasiliki Vita & Lambros Ekonomou, 2016. "Distributed Generation Islanding Effect on Distribution Networks and End User Loads Using the Load Sharing Islanding Method," Energies, MDPI, vol. 9(11), pages 1-24, November.
    4. Ifedayo Oladeji & Ramon Zamora & Tek Tjing Lie, 2021. "An Online Security Prediction and Control Framework for Modern Power Grids," Energies, MDPI, vol. 14(20), pages 1-27, October.
    5. Majid Ghaffarianfar & Amin Hajizadeh, 2018. "Voltage Stability of Low-Voltage Distribution Grid with High Penetration of Photovoltaic Power Units," Energies, MDPI, vol. 11(8), pages 1-13, July.
    6. Lizhen Wu & Yongnian Zhang & Xiaohong Hao & Wei Chen, 2020. "Research on a Location Method for Complex Voltage Sag Sources Based on Random Matrix Theory," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, May.
    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. Yunus Yalman & Tayfun Uyanık & Adnan Tan & Kamil Çağatay Bayındır & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Implementation of Voltage Sag Relative Location and Fault Type Identification Algorithm Using Real-Time Distribution System Data," Mathematics, MDPI, vol. 10(19), pages 1-13, September.
    2. Tayfun Uyanık & Yunus Yalman & Özcan Kalenderli & Yasin Arslanoğlu & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel," Mathematics, MDPI, vol. 10(22), pages 1-21, November.
    3. Michele Zanoni & Riccardo Chiumeo & Liliana Tenti & Massimo Volta, 2023. "What Else Do the Deep Learning Techniques Tell Us about Voltage Dips Validity? Regional-Level Assessments with the New QuEEN System Based on Real Network Configurations," Energies, MDPI, vol. 16(3), pages 1-24, January.

    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. Hamdy M. Sultan & Ahmed A. Zaki Diab & Oleg N. Kuznetsov & Ziad M. Ali & Omer Abdalla, 2019. "Evaluation of the Impact of High Penetration Levels of PV Power Plants on the Capacity, Frequency and Voltage Stability of Egypt’s Unified Grid," Energies, MDPI, vol. 12(3), pages 1-22, February.
    2. Karim M. El-Sharawy & Hatem Y. Diab & Mahmoud O. Abdelsalam & Mostafa I. Marei, 2021. "A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network," Sustainability, MDPI, vol. 13(11), pages 1-30, June.
    3. Vasiliki Vita & Georgios Fotis & Christos Pavlatos & Valeri Mladenov, 2023. "A New Restoration Strategy in Microgrids after a Blackout with Priority in Critical Loads," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    4. Sen Wang & Yonghui Sun & Yan Zhou & Rabea Jamil Mahfoud & Dongchen Hou, 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM," Energies, MDPI, vol. 13(1), pages 1-17, December.
    5. Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    6. Menghua Liu & Wei Zhao & Qing Wang & Songling Huang & Kunpeng Shi, 2019. "Compatibility Issues with Irregular Current Injection Islanding Detection Methods and a Solution," Energies, MDPI, vol. 12(8), pages 1-20, April.
    7. Shilpa Sambhi & Himanshu Sharma & Vikas Bhadoria & Pankaj Kumar & Ravi Chaurasia & Giraja Shankar Chaurasia & Georgios Fotis & Vasiliki Vita & Lambros Ekonomou & Christos Pavlatos, 2022. "Economic Feasibility of a Renewable Integrated Hybrid Power Generation System for a Rural Village of Ladakh," Energies, MDPI, vol. 15(23), pages 1-25, December.
    8. Simon Lineykin & Abhishek Sharma & Moshe Averbukh, 2023. "Eventual Increase in Solar Electricity Production and Desalinated Water through the Formation of a Channel between the Mediterranean and the Dead Sea," Energies, MDPI, vol. 16(11), pages 1-17, May.
    9. Tumiran Tumiran & Lesnanto Multa Putranto & Roni Irnawan & Sarjiya Sarjiya & Candra Febri Nugraha & Adi Priyanto & Ira Savitri, 2022. "Power System Planning Assessment for Optimizing Renewable Energy Integration in the Maluku Electricity System," Sustainability, MDPI, vol. 14(14), pages 1-25, July.
    10. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    11. Shilpa Sambhi & Himanshu Sharma & Vikas Bhadoria & Pankaj Kumar & Ravi Chaurasia & Georgios Fotis & Vasiliki Vita, 2023. "Technical and Economic Analysis of Solar PV/Diesel Generator Smart Hybrid Power Plant Using Different Battery Storage Technologies for SRM IST, Delhi-NCR Campus," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    12. Dahu Li & Xiaoda Cheng & Leijiao Ge & Wentao Huang & Jun He & Zhongwei He, 2022. "Multiple Power Supply Capacity Planning Research for New Power System Based on Situation Awareness," Energies, MDPI, vol. 15(9), pages 1-24, April.
    13. Shilpa Sambhi & Himanshu Sharma & Pankaj Kumar & Georgios Fotis & Vasiliki Vita & Lambros Ekonomou, 2022. "Techno-Economic Optimization of an Off-Grid Hybrid Power Generation for SRM IST, Delhi-NCR Campus," Energies, MDPI, vol. 15(21), pages 1-24, October.
    14. Luis Gerardo González & Rommel Chacon & Bernardo Delgado & Dario Benavides & Juan Espinoza, 2020. "Study of Energy Compensation Techniques in Photovoltaic Solar Systems with the Use of Supercapacitors in Low-Voltage Networks," Energies, MDPI, vol. 13(15), pages 1-15, July.
    15. Ali A. Radwan & Ahmed A. Zaki Diab & Abo-Hashima M. Elsayed & Hassan Haes Alhelou & Pierluigi Siano, 2020. "Active Distribution Network Modeling for Enhancing Sustainable Power System Performance; a Case Study in Egypt," Sustainability, MDPI, vol. 12(21), pages 1-22, October.
    16. Kihan Kim & Jin Hur, 2019. "Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources," Energies, MDPI, vol. 12(17), pages 1-13, August.
    17. Thomas Price & Gordon Parker & Gail Vaucher & Robert Jane & Morris Berman, 2022. "Microgrid Energy Management during High-Stress Operation," Energies, MDPI, vol. 15(18), pages 1-11, September.
    18. Stavros Lazarou & Vasiliki Vita & Lambros Ekonomou, 2018. "Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-17, November.
    19. Cheng Yan & Jianfeng Zhu & Xiuli Shen & Jun Fan & Dong Mi & Zhengming Qian, 2020. "Ensemble of Regression-Type and Interpolation-Type Metamodels," Energies, MDPI, vol. 13(3), pages 1-20, February.
    20. Seyed Morteza Alizadeh & Sakineh Sadeghipour & Cagil Ozansoy & Akhtar Kalam, 2020. "Developing a Mathematical Model for Wind Power Plant Siting and Sizing in Distribution Networks," Energies, MDPI, vol. 13(13), pages 1-34, July.

    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:15:y:2022:i:18:p:6641-:d:912210. 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.