IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i11p2686-d363272.html
   My bibliography  Save this article

Cloud Based IoT Solution for Fault Detection and Localization in Power Distribution Systems

Author

Listed:
  • Mussawir Ul Mehmood

    (Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Abasin Ulasyar

    (Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Abraiz Khattak

    (Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Kashif Imran

    (Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Haris Sheh Zad

    (Department of Electrical Engineering, Riphah International University, Islamabad 44000, Pakistan)

  • Shibli Nisar

    (Military College of Signals (MCS), National University of Sciences and Technology, Islamabad 44000, Pakistan)

Abstract

Power restoring time in power distribution systems (PDS) can be minimized by using efficient fault localization techniques. This paper proposes a novel, robust and scalable cloud based internet of things (IoT) solution for identification and localization of faults in PDS. For this purpose, a new algorithm is developed that can detect single and multiple simultaneous faults in the presence of single and multiple device or sensor failures. The algorithm has utilized a zone based approach that divides a PDS into different zones. A current sensing device (CSD) was deployed at the boundary of a zone. The function of CSD is to provide time synchronized current measurements and communicate with a cloud server through an edge device (ED). Another contribution of this research work is the unique implementation of context aware policy (CAP) in ED. Due to CAP, only those measurements are transmitted to cloud server that differ from the previously transmitted measurements. The cloud server performed calculations at regular intervals to detect faults in PDS. A relational database model was utilized to log various fault events that occur in PDS. An IEEE 37 node test feeder was selected as PDS to observe the performance of our solution. Two test cases were designed to simulate individual and multiple simultaneous faults in PDS. A third test case was implemented to demonstrate the robustness and scalability of proposed solution to detect multiple simultaneous faults in PDS when single and multiple sensor failures were encountered. It was observed that the new algorithm successfully localized the faults for all the three cases. Consequently, significant reductions were noticed in the amount of data that was sent to the cloud server. In the end, a comparison study of a proposed solution was performed with existing methods to further highlight the benefits of our technique.

Suggested Citation

  • Mussawir Ul Mehmood & Abasin Ulasyar & Abraiz Khattak & Kashif Imran & Haris Sheh Zad & Shibli Nisar, 2020. "Cloud Based IoT Solution for Fault Detection and Localization in Power Distribution Systems," Energies, MDPI, vol. 13(11), pages 1-19, May.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2686-:d:363272
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/11/2686/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/11/2686/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Milos Maryska & Petr Doucek & Pavel Sladek & Lea Nedomova, 2019. "Economic Efficiency of the Internet of Things Solution in the Energy Industry: A Very High Voltage Frosting Case Study," Energies, MDPI, vol. 12(4), pages 1-16, February.
    2. Saber Talari & Miadreza Shafie-khah & Pierluigi Siano & Vincenzo Loia & Aurelio Tommasetti & João P. S. Catalão, 2017. "A Review of Smart Cities Based on the Internet of Things Concept," Energies, MDPI, vol. 10(4), pages 1-23, March.
    3. Batista, N.C. & Melício, R. & Matias, J.C.O. & Catalão, J.P.S., 2013. "Photovoltaic and wind energy systems monitoring and building/home energy management using ZigBee devices within a smart grid," Energy, Elsevier, vol. 49(C), pages 306-315.
    4. Joao C. Ferreira & Ana Lucia Martins, 2019. "Edge Computing Approach for Vessel Monitoring System," Energies, MDPI, vol. 12(16), pages 1-15, August.
    5. Mahmood, Anzar & Javaid, Nadeem & Razzaq, Sohail, 2015. "A review of wireless communications for smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 248-260.
    6. Mojgan Hojabri & Ulrich Dersch & Antonios Papaemmanouil & Peter Bosshart, 2019. "A Comprehensive Survey on Phasor Measurement Unit Applications in Distribution Systems," Energies, MDPI, vol. 12(23), pages 1-23, November.
    7. Nagender Kumar Suryadevara & Gyan Ranjan Biswal, 2019. "Smart Plugs: Paradigms and Applications in the Smart City-and-Smart Grid," Energies, MDPI, vol. 12(10), pages 1-20, May.
    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. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
    2. Mina Farmanbar & Kiyan Parham & Øystein Arild & Chunming Rong, 2019. "A Widespread Review of Smart Grids Towards Smart Cities," Energies, MDPI, vol. 12(23), pages 1-18, November.
    3. Maharjan, Pukar & Salauddin, Md & Cho, Hyunok & Park, Jae Yeong, 2018. "An indoor power line based magnetic field energy harvester for self-powered wireless sensors in smart home applications," Applied Energy, Elsevier, vol. 232(C), pages 398-408.
    4. Marsal-Llacuna, Maria-Lluïsa, 2018. "Future living framework: Is blockchain the next enabling network?," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 226-234.
    5. Karina RADCHENKO, 2023. "The economic and social impacts of smart cities multi stakeholder pre study results," Smart Cities and Regional Development (SCRD) Journal, Smart-EDU Hub, vol. 7(2), pages 25-38, June.
    6. Wu, Xiaohua & Hu, Xiaosong & Yin, Xiaofeng & Zhang, Caiping & Qian, Shide, 2017. "Optimal battery sizing of smart home via convex programming," Energy, Elsevier, vol. 140(P1), pages 444-453.
    7. Gleb V. Savin, 2021. "The smart city transport and logistics system: Theory, methodology and practice," Upravlenets, Ural State University of Economics, vol. 12(6), pages 67-86, October.
    8. Carlos Toledo & Lucía Serrano-Lujan & Jose Abad & Antonio Lampitelli & Antonio Urbina, 2019. "Measurement of Thermal and Electrical Parameters in Photovoltaic Systems for Predictive and Cross-Correlated Monitorization," Energies, MDPI, vol. 12(4), pages 1-20, February.
    9. David Granados-Lieberman, 2020. "Global Harmonic Parameters for Estimation of Power Quality Indices: An Approach for PMUs," Energies, MDPI, vol. 13(9), pages 1-17, May.
    10. Diogo Abrantes & Marta Campos Ferreira & Paulo Dias Costa & Joana Hora & Soraia Felício & Teresa Galvão Dias & Miguel Coimbra, 2023. "A New Perspective on Supporting Vulnerable Road Users’ Safety, Security and Comfort through Personalized Route Planning," IJERPH, MDPI, vol. 20(4), pages 1-24, February.
    11. Colak, Ilhami & Kabalci, Ersan & Fulli, Gianluca & Lazarou, Stavros, 2015. "A survey on the contributions of power electronics to smart grid systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 562-579.
    12. Seung-Min Jung & Sungwoo Park & Seung-Won Jung & Eenjun Hwang, 2020. "Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities," Sustainability, MDPI, vol. 12(16), pages 1-20, August.
    13. Paterakis, Nikolaos G. & Erdinç, Ozan & Catalão, João P.S., 2017. "An overview of Demand Response: Key-elements and international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 871-891.
    14. Jing Zhang & Yiqi Li & Zhi Wu & Chunyan Rong & Tao Wang & Zhang Zhang & Suyang Zhou, 2021. "Deep-Reinforcement-Learning-Based Two-Timescale Voltage Control for Distribution Systems," Energies, MDPI, vol. 14(12), pages 1-15, June.
    15. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
    16. Do-In Kim, 2021. "Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network," Energies, MDPI, vol. 14(15), pages 1-15, July.
    17. Jun Qiu & Jing Cao & Xinyi Gu & Zimo Ge & Zhe Wang & Zheng Liang, 2023. "Design of an Evaluation System for Disruptive Technologies to Benefit Smart Cities," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
    18. Bingqian Zhang & Guochao Peng & Caihua Liu & Zuopeng Justin Zhang & Sajjad M. Jasimuddin, 2022. "Adaptation behaviour in using one-stop smart governance apps: an exploratory study between digital immigrants and digital natives," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1971-1991, December.
    19. Abubakar, I. & Khalid, S.N. & Mustafa, M.W. & Shareef, Hussain & Mustapha, M., 2017. "Application of load monitoring in appliances’ energy management – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 235-245.
    20. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.

    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:11:p:2686-:d:363272. 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.