983 resultados para Dominion Power Company
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Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.
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Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.
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This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.
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The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.
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Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques. © 2013 IEEE.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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The study of the micro-fauna of Montana formations has been almost entirely neglected. Because the petroleum industry of this state has not felt the necessity for using micro-paleontology in its sub-surface correlations, the science has been but little used. The Montana Power Company has had an examination made of some of its well cuttings by a competent micro-paleontologist who found some foraminifera in Mesozoic sediments. However, no investigations have been made to determine the presence and character of the micro-fauna of the Paleozoic formations of Montana.
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In this issue...Big Butte, "M" Days, Hotel Finlen, Congo region, Butte Business College, Miles City, Butte, Montana, Dr. Hult, commencement, Oratorical Contest
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In this issue...General Electric Company, Butte, Montana, Belmont Mine, Mountain Con Mine, Mines Debate Team," M" Annual, Montana Power Company
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In this issue...Anderson Carlisle Club, Mines Smoker, Rotary and Exchange Club, Kiwanis Club, Forestry policies, Elbert Hubbard, Rosenstein Brothers
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In this issue...Lois Fordmeir, Library, Mineral Club, Mining Engineering, Dale Barnum, Geological Society, Dillon, Montana, Lewis and Clark Caverns, Montana Power Company
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In this issue...Dr. A. E. Koenig, Magma Yearbook, Montana School of Mines Museum, M whitewash, Commencement, Anderson-Carlisle Technical Society, Gamer's Cafe
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In this issue...Wesley Salonen, M-Club, the Anaconda Company, Coach Ed Simonich, Mining Association of Montana, Copper Guards, Engineering Day
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In this issue...Copper Lounge, Union Pacific Railway, Air Force, Circle K Club, Petroleum Engineers, Montana Power Company, Historical Geology Field Trip, Lime Spur Quarry
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In this issue...United Nations meeting, Newman Club, Sylvia White, Veteran's Day, Glee Club, Montana Power Company, Harvest Festival Dance, Joe Knuckey's Melody Makers