Non-technical loss analysis and prevention using smart meters

In the countries such as Pakistan, for analyzing the losses and techniques in the power distribution and for mitigating, are the two active areas of research which is spread globally for increasing the accessibility of power irrespective of installing future generation equipment. As, the Technical Loss and the Non-Technical Loss are accounted by the total energy losses. They both are also referred as TL and NTLs. In terms of the non-technical losses there are major financial losses for the utility companies present in the countries that are in the developing stage. NTLs is the major cause for the additional losses and also it includes the part of damaging the network that includes the infrastructure and network reliability reduction. This paper is subjected to investigating the non-technical losses in terms of the power distribution systems. In addition to that, the consumer energy consumption information is used for analyzing the NTLs from Rawalpindi region from the different feeder source. The data of Low Voltage (LV) of the distribution network are focused more that consists of commercial, industrial, residential and agricultural consumers by the use of KWh interval data which is captured over a month using the smart meter infrastructure. The discussion of this review paper determines analysis and prevention techniques of NTLs to safeguard from the illegal use of the electricity in the distribution of electrical power system.

[1]  Jianmin Dong,et al.  A novel smooth Support Vector Machines for classification and regression , 2009, 2009 4th International Conference on Computer Science & Education.

[2]  João O. P. Pinto,et al.  Methodology for fraud detection using rough sets , 2006, 2006 IEEE International Conference on Granular Computing.

[3]  Francisco Herrera,et al.  Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: Experimental evaluation and support vector analysis , 2016, Appl. Soft Comput..

[4]  Petra Perner,et al.  Recent advances in data mining , 2006, Engineering applications of artificial intelligence.

[5]  Luigi Ferrigno,et al.  Experimental characterization of consensus protocol for decentralized smart grid metering , 2016 .

[6]  Jorge Coelho,et al.  Probabilistic methodology for Technical and Non-Technical Losses estimation in distribution system , 2013 .

[7]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[8]  J. Torriti,et al.  Demand response experience in Europe: Policies, programmes and implementation , 2010 .

[9]  F. Gubina,et al.  Allocation of the load profiles to consumers using probabilistic neural networks , 2005, IEEE Transactions on Power Systems.

[10]  Muhammad Azhar,et al.  Environmental and utility planning implications of electricity loss reduction in a developing country : A comparative study of technical options , 1998 .

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

[12]  S.K. Tiong,et al.  Non-Technical Loss analysis for detection of electricity theft using support vector machines , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[13]  Vinod Khadkikar,et al.  Application of Artificial Neural Networks for Shunt Active Power Filter Control , 2014, IEEE Transactions on Industrial Informatics.

[14]  H. Abdul Razak,et al.  Fault diagnosis on beam-like structures from modal parameters using artificial neural networks , 2015 .

[15]  Hong Zhang,et al.  A comprehensive support vector machine-based classification model for soil quality assessment , 2016 .

[16]  J. Stuart Aitken,et al.  Multiple algorithms for fraud detection , 2000, Knowl. Based Syst..

[17]  John McCarthy.  Phenomenal data mining , 2000, CACM.

[18]  Carlos León,et al.  Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection , 2014, Knowl. Based Syst..

[19]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[20]  R. Anand,et al.  e-metering solution for checking energy thefts and streamlining revenue collection in India , 2003, 2003 IEEE PES Transmission and Distribution Conference and Exposition (IEEE Cat. No.03CH37495).

[21]  Lingfeng Wang,et al.  Measures and setbacks for controlling electricity theft , 2010, North American Power Symposium 2010.

[22]  Phil Blythe,et al.  A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts , 2015 .

[23]  Mohammad Lutfi Othman,et al.  Rough-Set-and-Genetic-Algorithm based data mining and Rule Quality Measure to hypothesize distance protective relay operation characteristics from relay event report , 2011 .

[24]  Z. Vale,et al.  Data mining techniques application in power distribution utilities , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[25]  P. Wesley Schultz,et al.  Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms , 2015 .

[26]  J.O.P. Pinto,et al.  Fraud detection in high voltage electricity consumers using data mining , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[27]  Mercedes Ayuso,et al.  Modelling different types of automobile insurance fraud behaviour in the Spanish market , 1999 .

[28]  Caglar Yurtseven,et al.  The causes of electricity theft: An econometric analysis of the case of Turkey , 2015 .

[29]  Juan E. Tapiador,et al.  Power-aware anomaly detection in smartphones: An analysis of on-platform versus externalized operation , 2015, Pervasive Mob. Comput..

[30]  A.H. Nizar,et al.  Load profiling and data mining techniques in electricity deregulated market , 2006, 2006 IEEE Power Engineering Society General Meeting.

[31]  Lingfeng Wang,et al.  A conceptual design using harmonics to reduce pilfering of electricity , 2010, IEEE PES General Meeting.

[32]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[33]  Vishal Kumar,et al.  Genetic algorithm based support vector machine for on-line voltage stability monitoring , 2015 .

[34]  João Paulo Papa,et al.  Supervised pattern classification based on optimum-path forest , 2009 .

[35]  Assunta Sorrentino,et al.  Application of Artificial Neural Networks and Probability Ellipse methods for damage detection using Lamb waves , 2015 .

[36]  Chang-Tien Lu,et al.  Survey of fraud detection techniques , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[37]  K. Hasan,et al.  Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines , 2015, Psychiatry Research: Neuroimaging.

[38]  Wil L. Kling,et al.  Theft detection and smart metering practices and expectations in the Netherlands , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[39]  Greta Barnicoat,et al.  The ageing population and smart metering: A field study of householders’ attitudes and behaviours towards energy use in Scotland , 2015 .

[40]  Mahmoud Reza Sohrabi,et al.  The application of continuous wavelet transform and least squares support vector machine for the simultaneous quantitative spectrophotometric determination of Myricetin, Kaempferol and Quercetin as flavonoids in pharmaceutical plants. , 2016, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[41]  K. S. Swarup,et al.  Application benefits of Distribution Automation and AMI systems convergence methodology for distribution power restoration analysis , 2015 .

[42]  Rajanish K. Kamat,et al.  Modelling of nanostructured memristor device characteristics using Artificial Neural Network (ANN) , 2015, J. Comput. Sci..

[43]  Robert E. Schapire,et al.  How boosting the margin can also boost classifier complexity , 2006, ICML.

[44]  José Edison Cabral,et al.  Fraud detection in electrical energy consumers using rough sets , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[45]  Mahmoud Y. El-Bakry,et al.  Optoelectronic performance and artificial neural networks (ANNs) modeling of n-InSe/p-Si solar cell , 2015 .

[46]  Gennady Poda,et al.  Efficient variable selection batch pruning algorithm for artificial neural networks , 2015 .

[47]  Jianhua Xu,et al.  Multi-label Lagrangian support vector machine with random block coordinate descent method , 2016, Inf. Sci..

[48]  Mario J. Garcia,et al.  Microvascular obstruction detected by cardiac MRI after AMI for the prediction of LV remodeling and MACE: A meta-analysis of prospective trials. , 2016, International journal of cardiology.

[49]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[50]  Rhona Flin,et al.  Evaluation of the Scrub Practitioners' List of Intraoperative Non-Technical Skills system. , 2012, International journal of nursing studies.

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

[52]  Rüdiger W. Brause,et al.  Neural data mining for credit card fraud detection , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

[53]  João O. P. Pinto,et al.  Fraud identification in electricity company customers using decision tree , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[54]  Innocent E. Davidson.  Evaluation and effective management of nontechnical losses in electrical power networks , 2002, IEEE AFRICON. 6th Africon Conference in Africa,.

[55]  Lingfeng Wang,et al.  Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft , 2011 .

[56]  J.W. Fourie,et al.  A statistical method to minimize electrical energy losses in a local electricity distribution network , 2004, 2004 IEEE Africon. 7th Africon Conference in Africa (IEEE Cat. No.04CH37590).

[57]  A. Dick.  Theft of electricity-how UK electricity companies detect and deter , 1995 .

[58]  F. Mwaura,et al.  Adopting electricity prepayment billing system to reduce non-technical energy losses in Uganda: Lesson from Rwanda , 2012 .

[59]  M. Sforna,et al.  Data mining in a power company customer database , 2000 .

[60]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[61]  S. Daskalaki,et al.  Data mining for decision support on customer insolvency in telecommunications business , 2003, Eur. J. Oper. Res..

[62]  C. Senabre,et al.  Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps , 2006, IEEE Transactions on Power Systems.

[63]  Tom Fawcett,et al.  Adaptive Fraud Detection , 1997, Data Mining and Knowledge Discovery.

[64]  Lingfeng Wang,et al.  Support vector machine based data classification for detection of electricity theft , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[65]  Rong Jiang,et al.  Wavelet based feature extraction and multiple classifiers for electricity fraud detection , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[66]  Rhona Flin,et al.  Basic concepts for crew resource management and non-technical skills. , 2015, Best practice & research. Clinical anaesthesiology.

[67]  Kanungo Barada Mohanty,et al.  Fault classification in SEIG system using Hilbert-Huang transform and least square support vector machine , 2016 .

[68]  Birgit Mack,et al.  An analysis of smart metering information systems: A psychological model of self-regulated behavioural change , 2015 .

[69]  Thomas B. Smith,et al.  Electricity theft: a comparative analysis , 2004 .

[70]  Lingfeng Wang,et al.  High performance computing for detection of electricity theft , 2013 .

[71]  J.H.R. Enslin,et al.  Smarttrafo and Flicker , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[72]  Izham Zainal Abidin,et al.  Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm , 2007 .

[73]  Isabel Praça,et al.  Support Vector Machines for decision support in electricity markets' strategic bidding , 2016, Neurocomputing.

[74]  Z. Watral,et al.  Electromagnetic flow meters for open channels: Current state and development prospects , 2015 .

[75]  B. Neenan,et al.  Societal Benefits of Smart Metering Investments , 2008 .

[76]  Andrea Castelletti,et al.  Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review , 2015, Environ. Model. Softw..

[77]  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.