IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3110-d372295.html
   My bibliography  Save this article

Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System

Author

Listed:
  • Konstantinos V. Blazakis

    (School of Electrical and Computer Engineering, Technical University of Crete, University Campus, GR-73100 Chania, Greece)

  • Theodoros N. Kapetanakis

    (Department of Electronic Engineering, Hellenic Mediterranean University, GR-73100 Chania, Greece)

  • George S. Stavrakakis

    (School of Electrical and Computer Engineering, Technical University of Crete, University Campus, GR-73100 Chania, Greece)

Abstract

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.

Suggested Citation

  • Konstantinos V. Blazakis & Theodoros N. Kapetanakis & George S. Stavrakakis, 2020. "Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System," Energies, MDPI, vol. 13(12), pages 1-13, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3110-:d:372295
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3110/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3110/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    2. Viegas, Joaquim L. & Esteves, Paulo R. & Melício, R. & Mendes, V.M.F. & Vieira, Susana M., 2017. "Solutions for detection of non-technical losses in the electricity grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1256-1268.
    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. Saidjon Shiralievich Tavarov & Alexander Sidorov & Zsolt Čonka & Murodbek Safaraliev & Pavel Matrenin & Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane, 2023. "Control of Operational Modes of an Urban Distribution Grid under Conditions of Uncertainty," Energies, MDPI, vol. 16(8), pages 1-18, April.
    2. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    3. Francisco Jonatas Siqueira Coelho & Allan Rivalles Souza Feitosa & André Luís Michels Alcântara & Kaifeng Li & Ronaldo Ferreira Lima & Victor Rios Silva & Abel Guilhermino da Silva-Filho, 2023. "HyMOTree: Automatic Hyperparameters Tuning for Non-Technical Loss Detection Based on Multi-Objective and Tree-Based Algorithms," Energies, MDPI, vol. 16(13), pages 1-22, June.
    4. Hasan M. Salman & Jagadeesh Pasupuleti & Ahmad H. Sabry, 2023. "Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies," Sustainability, MDPI, vol. 15(20), pages 1-34, October.

    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. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    2. Marco Toledo-Orozco & Carlos Arias-Marin & Carlos Álvarez-Bel & Diego Morales-Jadan & Javier Rodríguez-García & Eddy Bravo-Padilla, 2021. "Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities," Energies, MDPI, vol. 14(4), pages 1-23, February.
    3. Sufian A. Badawi & Djamel Guessoum & Isam Elbadawi & Ameera Albadawi, 2022. "A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies," Mathematics, MDPI, vol. 10(11), pages 1-16, May.
    4. Lisardo Prieto González & Anna Fensel & Juan Miguel Gómez Berbís & Angela Popa & Antonio de Amescua Seco, 2021. "A Survey on Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(21), pages 1-16, November.
    5. Adnan Khattak & Rasool Bukhsh & Sheraz Aslam & Ayman Yafoz & Omar Alghushairy & Raed Alsini, 2022. "A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    6. Weijia Wen & Xiao Ling & Jianxin Sui & Junjie Lin, 2023. "Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning," Energies, MDPI, vol. 16(3), pages 1-15, January.
    7. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
    8. Hadjout, D. & Torres, J.F. & Troncoso, A. & Sebaa, A. & Martínez-Álvarez, F., 2022. "Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market," Energy, Elsevier, vol. 243(C).
    9. Akram Qashou & Sufian Yousef & Erika Sanchez-Velazquez, 2022. "Mining sensor data in a smart environment: a study of control algorithms and microgrid testbed for temporal forecasting and patterns of failure," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2371-2390, October.
    10. Xiaofeng Feng & Hengyu Hui & Ziyang Liang & Wenchong Guo & Huakun Que & Haoyang Feng & Yu Yao & Chengjin Ye & Yi Ding, 2020. "A Novel Electricity Theft Detection Scheme Based on Text Convolutional Neural Networks," Energies, MDPI, vol. 13(21), pages 1-17, November.
    11. Francisco Jonatas Siqueira Coelho & Allan Rivalles Souza Feitosa & André Luís Michels Alcântara & Kaifeng Li & Ronaldo Ferreira Lima & Victor Rios Silva & Abel Guilhermino da Silva-Filho, 2023. "HyMOTree: Automatic Hyperparameters Tuning for Non-Technical Loss Detection Based on Multi-Objective and Tree-Based Algorithms," Energies, MDPI, vol. 16(13), pages 1-22, June.
    12. Theyazn H. H. Aldhyani & Hasan Alkahtani, 2023. "Cyber Security for Detecting Distributed Denial of Service Attacks in Agriculture 4.0: Deep Learning Model," Mathematics, MDPI, vol. 11(1), pages 1-19, January.
    13. Mikhail A. Averbukh & Nikolay A. Zhukov & Stanislav V. Khvorostenko & Vasiliy I. Panteleev, 2019. "Reducing Electric Power Losses in the System of Power Supply Due to Compensation of Higher Harmonics of Currents: Economic and Energy Efficiency Outcomes," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 396-403.
    14. de Oliveira Ventura, Lucas & Melo, Joel D. & Padilha-Feltrin, Antonio & Fernández-Gutiérrez, Juan Pablo & Sánchez Zuleta, Carmen C. & Piedrahita Escobar, Carlos César, 2020. "A new way for comparing solutions to non-technical electricity losses in South America," Utilities Policy, Elsevier, vol. 67(C).
    15. Zeeshan Aslam & Nadeem Javaid & Ashfaq Ahmad & Abrar Ahmed & Sardar Muhammad Gulfam, 2020. "A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-24, October.
    16. Xuesong Tian & Yuping Zou & Xin Wang & Minglang Tseng & Hua Li & Huijuan Zhang, 2022. "Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    17. Cheong Hee Park & Taegong Kim, 2020. "Energy Theft Detection in Advanced Metering Infrastructure Based on Anomaly Pattern Detection," Energies, MDPI, vol. 13(15), pages 1-10, July.
    18. Stracqualursi, Erika & Rosato, Antonello & Di Lorenzo, Gianfranco & Panella, Massimo & Araneo, Rodolfo, 2023. "Systematic review of energy theft practices and autonomous detection through artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    19. Kong, Jun & Jiang, Wen & Tian, Qing & Jiang, Min & Liu, Tianshan, 2023. "Anomaly detection based on joint spatio-temporal learning for building electricity consumption," Applied Energy, Elsevier, vol. 334(C).
    20. Rui Xia & Yunpeng Gao & Yanqing Zhu & Dexi Gu & Jiangzhao Wang, 2022. "An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis," Energies, MDPI, vol. 15(19), pages 1-25, October.

    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:12:p:3110-:d:372295. 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.