Journal Article10.1016/J.KNOSYS.2014.08.014
Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection
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TL;DR: 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.
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Abstract: 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.
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Review of non-technical loss detection methods
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References
Demand response and smart grids—A survey
TL;DR: In this article, a survey of demand response potentials and benefits in smart grids is presented, with reference to real industrial case studies and research projects, such as smart meters, energy controllers, communication systems, etc.
2.3K
Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft
TL;DR: In this paper, the authors proposed an architectural design of smart meter, external control station, harmonic generator, and filter circuit to deject illegal consumers, and conserve and effectively utilize energy, where smart meters are designed to provide data of various parameters related to instantaneous power consumption.
376
•Book
Transmission and distribution electrical engineering
C.R. Bayliss
- 01 Jan 1996
TL;DR: In this article, the authors present drawings and diagrams of a system consisting of a Substation Layouts Substation Auxiliary Power Supplies Current and Voltage Transformers Insulators Substation Building Services Earthing and Bonding Insulation Co-ordination Relay Protection Fuses and Miniature Circuit Breakers Cables Switchgear Power Transformers Substation and Overhead Line Foundations overhead line Routing Structures, Towers and Poles Overhead line Conductor and Technical Specifications Testing and Commissioning Electromagnetic Compatibility System Control and Data Acquisition Project Management Fundamentals.
254
Smart power grid and cloud computing
TL;DR: How Cloud computing model can be used for developing Smart Grid solutions, based on the delivery of computing as a service, whereby storage, software and information are provided to computers and other devices as a commodity over the Internet is discussed.
232
Links to the Future: Communication Requirements and Challenges in the Smart Grid
TL;DR: In this paper, the authors present the communication requirements of the smart grid and identify the main technical challenges that need to be tackled, and the only viable way to fulfill these requirements is to design a new communication architecture that can support smart grid services and control operations.
229