12,676 research outputs found
A mining framework to detect non-technical losses in power utilities
This paper deals with the characterization of customers in power companies in order to detect consumption
Non-Technical Losses (NTL). A new framework is presented, to find relevant knowledge about the particular
characteristics of the electric power customers. The authors uses two innovative statistical estimators to weigh
variability and trend of the customer consumption. The final classification model is presented by a rule set,
based on discovering association rules in the data. The work is illustrated by a case study considering a real
data base
Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
Currently, power distribution companies have several problems that are related to energy losses. For
example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s
measurement equipment. These types of losses are called non-technical losses (NTLs), and these
losses are usually greater than the losses that are due to the distribution infrastructure (technical losses).
Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our
knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created
based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is
based on the knowledge and expertise of the inspectors and that uses text mining, neural networks,
and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques
were used to extract information from samples, and this information was translated into rules,
which were joined to the rules that were generated by the knowledge of the inspectors. This system
was tested with real samples that were extracted from Endesa databases. Endesa is one of the most
important distribution companies in Spain, and it plays an important role in international markets in
both Europe and South America, having more than 73 million customers
A real application on non-technical losses detection: the MIDAS Project
The MIDAS project began at 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 test phase and it is applied in real cases in company databases
Detection of Non-Technical Losses in Smart Distribution Networks: a Review
With the advent of smart grids, distribution utilities have
initiated a large deployment of smart meters on the premises of the
consumers. The enormous amount of data obtained from the consumers
and communicated to the utility give new perspectives and possibilities
for various analytics-based applications. In this paper the current
smart metering-based energy-theft detection schemes are reviewed and
discussed according to two main distinctive categories: A) system statebased,
and B) arti cial intelligence-based.Comisión Europea FP7-PEOPLE-2013-IT
Detection of Non-Technical Losses: The Project MIDAS
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’ fa cilities. 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
Heterogeneous data source integration for smart grid ecosystems based on metadata mining
The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.Ministerio de Economía y Competitividad TEC2013-40767-
Solutions for detection of non-technical losses in the electricity grid: a review
This paper is a review of literature with an analysis on a selection of scienti c
studies for detection of non-technical losses. Non-technical losses occurring
in the electric grid at level of transmission or of distribution have negative impact on economies, affecting utilities, paying consumers and states.
The paper is concerned with the lines of research pursued, the main techniques
used and the limitations on current solutions. Also, a typology for
the categorization of solutions for detection of non-technical losses is proposed and the sources and possible attack/vulnerability points are identifi ed. The selected literature covers a wide range of solutions associated with
non-technical losses. Of the 103 selected studies, 6 are theoretical, 25 propose
hardware solutions and 72 propose non-hardware solutions. Data based
classi cation models and data from consumption with high resolution are
respectively required in about 47% and 35% of the reported solutions. Available
solutions cover a wide range of cases, with the main limitation found being the lack of an uni ed solution, which enables the detection of all kinds of non-technical losses
Increasing the efficiency in non-technical losses detection in utility companies
Usually, the fraud detection method in utility
companies uses the consumption information, the economic
activity, the geographic location, the active/reactive ration and
the contracted power. This paper proposes a combined text
mining and neural networks to increase the efficiency in NonTechnical
Losses (NTLs) detection methods which was
previously applied. This proposed framework proposes to collect
all the information that normally cannot be treated with
traditional methods. This framework is part of a research
project. This project is done in collaboration with Endesa, one of
the most important power distribution companies of Europe.
Currently, the proposed framework is in the test stage and it uses
real cases
A Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries
According to statistics, developing countries all over the world have suffered significant non-technical losses (NTLs) both in natural gas and electricity distribution. NTLs are thought of as energy that is consumed but not billed e.g., theft, meter tampering, meter reversing, etc. The adaptation of smart metering technology has enabled much of the developed world to significantly reduce their NTLs. Also, the recent advancements in machine learning and data analytics have enabled a further reduction in these losses. However, these solutions are not directly applicable to developing countries because of their infrastructure and manual data collection. This paper proposes a tailored solution based on machine learning to mitigate NTLs in developing countries. The proposed method is based on a multivariate Gaussian distribution framework to identify fraudulent consumers. It integrates novel features like social class stratification and the weather profile of an area. Thus, achieving a significant improvement in fraudulent consumer detection. This study has been done on a real dataset of consumers provided by the local power distribution companies that have been cross-validated by onsite inspection. The obtained results successfully identify fraudulent consumers with a maximum success rate of 75%. 2013 IEEE.This work was supported by the Qatar National Library.Scopus2-s2.0-8510734936
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