On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization

According to The Brazilian Electricity Regulatory Agency, Brazil reached a loss of approximately U.S.$ 4 billion in commercial losses during 2011, which correspond to more than 27 000 GWh. The strengthening of the smart grid has brought a considerable amount of research that can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically detect commercial losses, but the problem of selecting the most representative features has not been widely discussed. In this paper, we make a parallel among the problem of commercial losses in Brazil and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm. The experimental setup is conducted over two private datasets (commercial and industrial) provided by a Brazilian electric utility, and it shows the importance of selecting the most relevant features in the context of theft characterization.

[1]  M.E. de Oliveira,et al.  A statistical analysis of loss factor to determine the energy losses , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America.

[2]  João Paulo Papa,et al.  Supervised pattern classification based on optimum‐path forest , 2009, Int. J. Imaging Syst. Technol..

[3]  C C O Ramos,et al.  A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest , 2011, IEEE Transactions on Power Systems.

[4]  Farrukh Nagi,et al.  Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System , 2011, IEEE Transactions on Power Delivery.

[5]  Abdolreza Hatamlou,et al.  Black hole: A new heuristic optimization approach for data clustering , 2013, Inf. Sci..

[6]  Sieh Kiong Tiong,et al.  Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines , 2010, IEEE Transactions on Power Delivery.

[7]  João Paulo Papa,et al.  Efficient supervised optimum-path forest classification for large datasets , 2012, Pattern Recognit..

[8]  Carlos León,et al.  MIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniques , 2006, ICCSA.

[9]  Chan-Nan Lu,et al.  Non-technical loss detection using state estimation and analysis of variance , 2013, 2013 IEEE Power & Energy Society General Meeting.

[10]  J. A. Porras,et al.  Identification of non-technical electricity losses in power distribution systems by applying techniques of information analysis and visualization , 2015, IEEE Latin America Transactions.

[11]  R. Alves,et al.  Reduction of Non-Technical Losses by Modernization and Updating of Measurement Systems , 2006, 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America.

[12]  Ron Millard,et al.  Non Technical Losses – How do other countries tackle the problem ? , 2009 .

[13]  Caio C. O. Ramos,et al.  Fast Non-Technical Losses Identification Through Optimum-Path Forest , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[14]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

[15]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[16]  V. Paruchuri,et al.  An approach to determine non-technical energy losses in India , 2012, 2012 14th International Conference on Advanced Communication Technology (ICACT).

[17]  C. C. O. Ramos,et al.  New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection , 2012, IEEE Transactions on Power Delivery.

[18]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[19]  João Paulo Papa,et al.  Black Hole Algorithm for non-technical losses characterization , 2015, 2015 IEEE 6th Latin American Symposium on Circuits & Systems (LASCAS).

[20]  Z. Geem.  Music-Inspired Harmony Search Algorithm: Theory and Applications , 2009 .

[21]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[22]  Antonio Irpino,et al.  Supervised classification of distributed data streams for smart grids , 2012 .

[23]  João Paulo Papa,et al.  A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection , 2011, Comput. Electr. Eng..

[24]  James W. Hardin,et al.  Exact Wilcoxon Signed-Rank and Wilcoxon Mann–Whitney Ranksum Tests , 2013 .

[25]  A.H. Nizar,et al.  Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method , 2008, IEEE Transactions on Power Systems.

[26]  Zhao Yang Dong,et al.  Customer Information System Data Pre-Processing with Feature Selection Techniques for Non-Technical Losses Prediction in an Electricity Market , 2006, 2006 International Conference on Power System Technology.