Monitoring technical losses to improve non-technical losses estimation and detection in LV distribution systems

被引:16
|
作者
Henriques, H. O. [1 ]
Correa, R. L. S. [1 ]
Fortes, M. Z. [1 ]
Borba, B. S. M. C. [1 ]
Ferreira, V. H. [1 ]
机构
[1] Fluminense Fed Univ UFF, Elect Engn Dep TEE, BR-24210240 Niteroi, RJ, Brazil
关键词
Non-technical losses; Smart grids; Temperature sensors; Low voltage distribution; Backward/forward sweep; Power summation method; ELECTRICITY THEFT;
D O I
10.1016/j.measurement.2020.107840
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The process of mitigating non-technical losses (NTL) in power distribution utilities is done in two stages. The first determines which distribution transformers have high NTL values. The second attempts to locate fraudulent consumers, powered by these transformers. This paper proposes a new methodology to improve the calculation of technical losses (TL), leading to a better estimation of the NTL, using temperature sensors. It is also presented a new process to identify possible energy theft locations using voltage drop differences. The identification of possible energy pilfering spots is done with the aid of the measurement of technical losses obtained in the first stretches of the low voltage network, near the transformer, where the TL is more significant. A Backward/Forward Sweep algorithm using the power summation technique is used to calculate the voltage drops in two situations: with power data read only from the smart meters and power data including the TL readings. An analysis of the differences in voltage drop at each point between the two situations makes it possible to locate the probable energy thief. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:11
相关论文
共 50 条
  • [31] Non-Technical Losses Review and Possible Methodology Solutions
    Hammerschmitt, Bruno Knevitz
    Abaide, Alzenira da Rosa
    Lucchese, Felipe Cirolini
    Martins, Criciele Castro
    da Silveira, Alexandre Schopf
    Rigodanzo, Jonas
    Castro, Joao Vitor Maccari Brabo
    Rohr, Julio Affonso Dall Agnol
    2020 6TH INTERNATIONAL CONFERENCE ON ELECTRIC POWER AND ENERGY CONVERSION SYSTEMS (EPECS 2020), 2020, : 64 - 68
  • [32] Black Hole Algorithm for Non-technical Losses Characterization
    Rodrigues, Douglas
    Oba Ramos, Caio Cesar
    de Souza, Andre Nunes
    Papa, Joao Paulo
    2015 IEEE 6TH LATIN AMERICAN SYMPOSIUM ON CIRCUITS & SYSTEMS (LASCAS), 2015,
  • [33] Explainable Artificial Intelligence for Prediction of Non-Technical Losses in Electricity Distribution Networks
    Nwafor, Obumneme
    Okafor, Emmanuel
    Aboushady, Ahmed A.
    Nwafor, Chioma
    Zhou, Chengke
    IEEE ACCESS, 2023, 11 : 73104 - 73115
  • [34] Non-technical losses detection with Gramian angular field and deep residual network
    Chen, Yuhui
    Li, Jian
    Huang, Qi
    Li, Ke
    Zhao, Zixu
    Ren, Xibi
    ENERGY REPORTS, 2023, 9 : 1392 - 1401
  • [35] Detection of Non-Technical Losses Using Smart Meter Data and Supervised Learning
    Mihaela Buzau, Madalina
    Tejedor-Aguilera, Javier
    Cruz-Romero, Pedro
    Gomez-Exposito, Antonio
    IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (03) : 2661 - 2670
  • [36] A Probabilistic Optimum-Path Forest Classifier for Non-Technical Losses Detection
    Fernandes, Silas E. N.
    Pereira, Danillo R.
    Ramos, Caio C. O.
    Souza, Andre N.
    Gastaldello, Danilo S.
    Papa, Joao P.
    IEEE TRANSACTIONS ON SMART GRID, 2019, 10 (03) : 3226 - 3235
  • [37] Detection of Non-Technical Losses in Power Utilities-A Comprehensive Systematic Review
    Saeed, Muhammad Salman
    Mustafa, Mohd Wazir
    Hamadneh, Nawaf N.
    Alshammari, Nawa A.
    Sheikh, Usman Ullah
    Jumani, Touqeer Ahmed
    Khalid, Saifulnizam Bin Abd
    Khan, Ilyas
    ENERGIES, 2020, 13 (18)
  • [38] Large-Scale Detection of Non-Technical Losses In Imbalanced Data Sets
    Glauner, Patrick
    Boechat, Andre
    Dolberg, Lautaro
    State, Radu
    Bettinger, Franck
    Rangoni, Yves
    Duarte, Diogo
    2016 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2016,
  • [39] Descriptive Data Analysis of Weather Inputs for Non-Technical Losses Detection System
    Capeletti, Marcelo Bruno
    Abaide, Alzenira Da Rosa
    Hammerschmitt, Bruno Knevitz
    Neto, Nelson Knak
    Callai Dos Santos, Laura Lisiane
    Milbradt, Rafael Gressler
    Kaehler Guarda, Fernando Guilherme
    Prade, Lucio Rene
    Moreira, Gabriel Da Rosa
    PROCEEDINGS OF 9TH INTERNATIONAL CONFERENCE ON MODERN POWER SYSTEMS (MPS 2021), 2021,
  • [40] Big data analytics: an aid to detection of non-technical losses in power utilities
    Micheli, Giovanni
    Soda, Emiliano
    Vespucci, Maria Teresa
    Gobbi, Marco
    Bertani, Alessandro
    COMPUTATIONAL MANAGEMENT SCIENCE, 2019, 16 (1-2) : 329 - 343