Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees

• Published in: International journal of electrical power & energy systems Vol. 34; no. 1; pp. 90 - 98
• Main Authors: Monedero, Iñigo, Biscarri, Félix, León, Carlos, Guerrero, Juan I., Biscarri, Jesús, Millán, Rocío
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 2012; Elsevier
• Subjects: Applied sciences; Bayesian analysis; Bayesian network; Coefficients; Customers; Data mining; Decision tree; Decision trees; Electric power generation; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Networks; Non-technical loss; Operation. Load control. Reliability; Pearson correlation coefficient; Power networks and lines; Utilities; Europe; America; Latin America; Spain; Non-technical loss; Data mining; Decision tree; Bayesian network; Pearson correlation coefficient; Correlation coefficient; Power system management; Clientele; Electric utility; Algorithm; Fraud detection; Database; Electric loss; Bayes decision
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2011.09.009

► We develop algorithms for the detection of non-technical losses in a power utility. ► We detect customers with consumption drops with the use of the Pearson coefficient. ► We detect NTLs with Bayesian networks and decision trees. ► We validated the algorithms with real customers of the database of Endesa Company. For the electrical sector, minimizing non-technical losses is a very important task because it has a high impact in the company profits. Thus, this paper describes some new advances for the detection of non-technical losses in the customers of one of the most important power utilities of Spain and Latin America: Endesa Company. The study is within the framework of the MIDAS project that is being developed at the Electronic Technology Department of the University of Seville with the funding of this company. The advances presented in this article have an objective of detecting customers with anomalous drops in their consumed energy (the most-frequent symptom of a non-technical loss in a customer) by means of a windowed analysis with the use of the Pearson coefficient. On the other hand, besides Bayesian networks, decision trees have been used for detecting other types of patterns of non-technical loss. The algorithms have been tested with real customers of the database of Endesa Company. Currently, the system is in operation.