Navegando por Assunto "Decision tree"

Agora exibindo 1 - 8 de 8

Acesso aberto (Open Access)
Avaliação da distorção harmônica total de tensão no ponto de acoplamento comum industrial usando o processo KDD baseado em medição
(Universidade Federal do Pará, 2018-03-27) OLIVEIRA, Edson Farias de; TOSTES, Maria Emília de Lima; http://lattes.cnpq.br/4197618044519148
In the last decades, the transformation industry has provided the introduction of increasingly faster and more energy efficient products for residential, commercial and industrial use, however these loads due to their non-linearity have contributed significantly to the increase of distortion levels harmonic of voltage as a result of the current according to the Power Quality indicators of the Brazilian electricity distribution system. The constant increase in the levels of distortions, especially at the point of common coupling, has generated in the current day a lot of concern in the concessionaires and in the consumers of electric power, due to the problems that cause like losses of the quality of electric power in the supply and in the installations of the consumers and this has provided several studies on the subject. In order to contribute to the subject, this thesis proposes a procedure based on the Knowledge Discovery in Database - KDD process to identify the impact loads of harmonic distortions of voltage at the common coupling point. The proposed methodology uses computational intelligence and data mining techniques to analyze the data collected by energy quality meters installed in the main loads and the common coupling point of the consumer and consequently establish the correlation between the harmonic currents of the nonlinear loads with the harmonic distortion at the common coupling point. The proposed process consists in analyzing the loads and the layout of the location where the methodology will be applied, in the choice and installation of the QEE meters and in the application of the complete KDD process, including the procedures for collection, selection, cleaning, integration, transformation and reduction, mining, interpretation, and evaluation of data. In order to contribute, the data mining techniques of Decision Tree and Naïve Bayes were applied and several algorithms were tested for the algorithm with the most significant results for this type of analysis as presented in the results. The results obtained evidenced that the KDD process has applicability in the analysis of the Voltage Total Harmonic Distortion at the Point of Common Coupling and leaves as contribution the complete description of each step of this process, and for this it was compared with different indices of data balancing, training and test and different scenarios in different shifts of analysis and presented good performance allowing their application in other types of consumers and energy distribution companies. It also shows, in the chosen application and using different scenarios, that the most impacting load was the seventh current harmonic of the air conditioning units for the collected data set.
Acesso aberto (Open Access)
Comparação dos algoritmos C4.5 e MLP usados na avaliação da segurança dinâmica e no auxílio ao controle preventivo no contexto da estabilidade transitória de sistemas de potência
(Universidade Federal do Pará, 2013-03-06) OLIVEIRA, Werbeston Douglas de; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813; VIEIRA, João Paulo Abreu; http://lattes.cnpq.br/8188999223769913
This work compares the C4.5 and multilayer perceptron (MLP) algorithms applied for dynamic security assessment (DSA) and power system stability transient preventive control design. C4.5 is an algorithm of the decision tree (DT) technique and the MLP is a member of artificial neural network (ANNs) family. The advent of DTs and ANNs provides solution to real-time DSA issues in order to identify quickly when a power system is subjected to a critical disturbance (short-circuit) that may lead to transient instability. In addition, the knowledge obtained by both techniques can be utilized in the preventive control design to restore the power system security against critical disturbances. Based on the data base generation with exhaustive time-domain simulations, some specific critical disturbances are taken as examples to compare the C4.5 and MLP algorithms employed to DSA and guideline to preventive actions. The comparative study is tested on the New England power system. In the case studies, the knowledge database is generated by using PSTv3 (Power System Toolbox) software. The DTs and ANNs are trained and tested by the Rapidminer software. The obtained results have demonstrated a promising application of the C4.5 and MLP algorithms used in power system DSA and preventive control design.
Acesso aberto (Open Access)
Despacho ótimo de redes integradas de energia elétrica e de gás natural com restrições de segurança via árvores de decisão
(Universidade Federal do Pará, 2016-12-07) COSTA, Denis Carlos Lima; VIEIRA, João Paulo Abreu; http://lattes.cnpq.br/8188999223769913; NUNES, Marcus Vinícius Alves; http://lattes.cnpq.br/9533143193581447
This thesis proposes the use of a decision tree (DT) based security dispatch method applied to integrated electric power and natural-gas networks (IPGN) against credible contingencies that may cause violations. Preventive adjustments to the optimal electric energy generation and gas production are carried out based on the security regions and boundaries of controllable variables determined by the DTs. The easily interpretable DT’s rules that describe the security regions are tractable constraints to be included in the optimization routines of electricity generation and gas production rescheduling. Some specific critical contingencies applied to the IEEE 118-bus test system integrated with the 15-node natural gas network are taken as examples to demonstrate a promising application of the proposed security dispatch method to restore IPGN security.
Acesso aberto (Open Access)
Detecção de fraudes no consumo de energia elétrica usando árvores de decisão
(Universidade Federal do Pará, 2017-07-11) MATOS, Yasmin Christine Correa; VIEIRA, João Paulo Abreu; http://lattes.cnpq.br/8188999223769913
In recent years, the injury caused by the nontechnical losses to power distribution utilities, in Brazil have been estimated at R$ 7 billion per year. This reality represents a challenge for some of country’s utilities, who need effective measures to combat commercial losses. In this scenario, this dissertation presents a methodology able of detecting fraud in the consumption of electric energy, using a technique of data mining, known as decision tree. Performance tests of the method were done using real data from the history of electricity consumption and the inspection of consumer units (CU’s) suspected of being irregular in the metropolitan region of Belém. The results showed that the proposed decision-tree based method performs well in the detection of fraud in the electric power consumption.
Acesso aberto (Open Access)
Experimentos de mineração de dados aplicados a sistemas scada de usinas hidrelétricas
(Universidade Federal do Pará, 2012-04-13) OHANA, Ivaldo; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813
The current model of the Brazilian electric sector allows equal terms to all actors and reduces the role of the State in this sector. This model forces the electrical utilities to improve the quality of their products and, as a prerequisite for this purpose, they should make more effective use of the enormous amount of operational data that are stored in databases, acquired from the operation of their electrical systems which use the hydroelectric power plants as their main source of energy generation. One of the main tools for managing the operation of these plants are the Supervisory Control and Data Acquisition systems (SCADA). Thus, the large amount of data stored in databases by SCADA systems, certainly containing relevant information, should be treated to discover relationships and patterns that would help in the understanding of many important operational aspects as well as in the evaluation of operational performance of the electric power systems. The process of Knowledge Discovery in Database (KDD) is the process of identification of patterns in large data sets, that are valid, new, and useful to improve the understanding of a problem or a decision-making procedure. Data Mining is the step within KDD that extracts useful information from large databases. In this scenario, the present study objective is to perform data mining experiments on data generated by power plants SCADA systems, to produce relevant information to assist in planning, operation, maintenance and security of hydro power plants and also contribute to the implementation of the culture of using data mining techniques applied to these plants.
Acesso aberto (Open Access)
Metodologia de controle preventivo baseado em árvore de decisão para a melhoria da segurança estática e dinâmica de sistemas elétricos de potência
(Universidade Federal do Pará, 2015-12-05) GAIA, Dieigo Sá; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813; VIEIRA, João Paulo Abreu; http://lattes.cnpq.br/8188999223769913
This work aims to present a set of computational tools to support the real time operation and preventive control to enhance the static and dynamic security of power systems. The data mining technique known as decision tree was utilized to determine power system operating state as well as to provide operating guidelines for the preventing control actions necessary to avoid continuous decline of bus voltages and transient instability problems. Preliminary tests were carried out using operation historical data collected by SCADA/SAGE host located at Eletrobrás Eletronorte's regional control center. The obtained results validated the set of computational tools and also demonstrated the prospective application in real time operating environment.
Acesso aberto (Open Access)
Uso de árvore de decisão para avaliação da segurança estática em tempo real de sistemas elétricos de potência
(Universidade Federal do Pará, 2014-09-12) RODRIGUES, Benedito das Graças Duarte; VIEIRA, João Paulo Abreu; http://lattes.cnpq.br/8188999223769913; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813
The techniques used to Static Security Assessment in power systems depend on the implementation of a large number of cases of load flow for various topologies and system operating conditions. In real-time operation environments, this practice is difficult to implement, especially in large systems where the execution of all cases of load flow needed, requires high time and computational effort even for the current resources available. Data Mining techniques such as decision tree have been used in recent years and have achieved good results in the applications of static and dynamic security assessment of electrical power systems. This work presents a methodology for static security assessment in real-time of electrical power systems using the decision tree, where off-line load flow simulations, performed by software ANAREDE (CEPEL), has been generated an extensive labeled database related to the state of the system for various operating conditions. This database was used for induction of decision trees, providing a model for fast and accurate prediction that classifies the state of the system (secure or insecure) for real time application. This methodology reduces the use of computers in the on-line environment, since the processing of the decision tree requires only checking some if-then logical instructions of a limited number of numerical tests in the binary nodes for the attribute value definition that satisfies the rules, because these tests are performed in a same number of hierarchical levels of the decision tree, which is usually reduced. With this simple computational processing, the task of the static security evaluating will be able to be performed in a fraction of the time required to perform by faster traditional methods. To validate the methodology, a case study based on a real power system was performed, where for every contingency classified as insecure a corrective control action was executed from the decision tree information on the critical attribute that affects the security. The results showed the methodology is an important tool for static security assessment in real time for use in a center's operation system.
Acesso aberto (Open Access)
Uso de técnicas de mineração de dados para a extração de indicação de falha na operação de hidrogeradores a partir de medidas de descargas parciais
(Universidade Federal do Pará, 2016-06-17) PARDAUIL, Ana Carolina Neves; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813
By studies conducted by CIGRE in 2009, it was found that the main source of hydro generator failures is correlated to the machine electrical insulation. Due to this fact, monitoring the stator winding conditions became an important supervising procedure. A very used practice to accomplish this supervision is through the measurement and analysis of partial discharges (PDs), being this practice one of the most effective and secure methods for analysis of generator stator insulation. However, although PDs have well-defined standards, it is not trivial to classify the obtained PDs signals in these patterns, mainly due to the large number and variety of PDs occurrences. Today, the significant increase in the amount of PDs data available was due to improvements in equipment and software for PDs monitoring, as for example the system IMA-DP, which has contributed to better planned and more frequent measurement campaigns. So, this work proposes the use of an intelligent tool to facilitate the process of identification and diagnosis of partial discharges, based on data mining techniques using decision trees (DT), which is a solution for analyzing large amount of data. In the specific case presented in this dissertation it was used 2,435 measurements obtained for phase A of a hydro generator of the Tucurui Hydro Plant, which was essential to validate the proposed method, because they represent real data obtained from the Hydro Plant operation. A hybrid approach (supervised/unsupervised) was used to identify and rank PDs patterns among the well-known forms of DPs. A fast and very satisfactory PDs classification procedure was achieved, especially when converting data from statistical maps into amplitude histograms, thus, obtaining well-defined clusters and a created decision tree that achieved global indices of accuracy above 98%.