Navegando por Assunto "Power plants"

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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)
Uma nova solução para a otimização do despacho econômico e ambiental utilizando metaheurísticas da computação bio-inspirada
(Universidade Federal do Pará, 2016) NASCIMENTO, Manoel Henrique Reis; NUNES, Marcus Vinícius Alves; http://lattes.cnpq.br/9533143193581447
Due to the significant industrial growth in the North of Brazil, especially at the Industrial Pole of Manaus (PIM), it has been an increased necessity for energy generation, which in this region is provided by thermoelectric plants (UTEs) in over 90% of its total. Thus, it became necessary the use of computational tools that help the specialists or operators of electrical systems, for making decisions about the optimal power dispatch of each generating unit that contemplate not only to reduce costs but also reduce the atmospheric pollution levels. Optimization of Economic Dispatch (ED) is one of the oldest and most important tasks in power plant management, and currently, due to growing concerns about the environment, this problem is extended to the optimization of the Economic and Environmental Dispatch (EAD). This thesis has as main objective to analyze a new proposal to solve the old optimization problem of ED and the EAD implemented by several Deterministic methods (Iteration Lambda, Quadratic Programming and Newton method) and Heuristic methods (Genetic Algorithms, Particle Swarm, Differential evolution, Simulated Annealing, Optimization by Grey Wolf and Artificial Bee Colonies) for the ED problem. Non-dominated Sorting Genetic Algorithms (NSGA II and NSGA III), were used for evaluating the problem of EAD, considering the shutdown of the generators with higher losses and thus reducing the fuel cost. The method of incremental cost and transmission losses are used to determine the best active power values for each generating unit. It was ensured the energy balance between the total generated power, the demand of the electrical system, losses and minimizing, on the other hand, the total cost of fuel, reducing emissions, and further improving efficiency not only for generators but also to UTE as a whole. Consequently, the proposed new solution has the following contributions: contemplates the turning off generation systems that have higher fuel cost, reducing the overall costs and enabling predictive maintenance on these machines. This approach also determines optimal solutions for the power output in various scenarios characteristic and not characteristic of UTEs or power plants, considering changes in active power generation and reducing greenhouse gas emissions as NOx and CO2. To explore the feasibility of the new solution proposed by this theory, it was used as a test system a set of ten (10) generating units for the case study and three sets of generators´ parameters described in the literature. They were used for demonstrating the robustness of the proposed solution considering the use of various deterministic and Bioinspired computing methods for mono-objective and multi-objective optimization. The results presented here, from an analysis of several practical examples show the advantages of the new proposed solution.