Detection of Non-Technical Losses-Reference-Cited by-同舟云学术

Detection of Non-Technical Losses

Published:2014 Issue: Volume: Page:140-164
ISSN:1948-9730
Container-title:Advances in Secure Computing, Internet Services, and Applications
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Author:

Guerrero Juan I.¹,Monedero Íñigo¹,Biscarri Félix¹,Biscarri Jesús¹,Millán Rocío¹,León Carlos¹

Affiliation:

1. Universidad de Sevilla, Spain

Abstract

The MIDAS project began in 2006 as collaboration between Endesa, Sadiel, and the University of Seville. The objective of the MIDAS project is the detection of Non-Technical Losses (NTLs) on power utilities. The NTLs represent the non-billed energy due to faults or illegal manipulations in clients’ facilities. Initially, research lines study the application of techniques of data mining and neural networks. After several researches, the studies are expanded to other research fields: expert systems, text mining, statistical techniques, pattern recognition, etc. These techniques have provided an automated system for detection of NTLs on company databases. This system is in the test phase, and it is applied in real cases in company databases.

Publisher

IGI Global

Reference72 articles.

1. Assessment of the smart grids applied in reducing the cost of distribution system losses

2. CARDWATCH: a neural network based database mining system for credit card fraud detection

3. Reduction of Non-Technical Losses by Modernization and Updating of Measurement Systems

4. Probabilistic methodology for Technical and Non-Technical Losses estimation in distribution system

5. Biscarri, F., Monedero, Í., León, C., Guerrero, J. I., & Biscarri, J. (2009). A mining framework to detect non-technical losses in power utilities. In J. Cordeiro & J. Filipe (Eds.), Proceedings of the 11th International Conference on Enterprise Information Systems (pp. 96–101). IEEE.

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