Livros e capítulo

URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/6

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Filtros

2017 - 20193

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Data inicial: 2017Data final: 2019

Resultados da Pesquisa

Agora exibindo 1 - 3 de 3
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    Different Deterministic Optimization Methods for Economic Load Dispatch, Switching Off Less Efficient Generators
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2017) LEITE, Jandecy Cabral; CAMPOS, Paola Souto
    The Economic Load Dispatch (ELD) is one of the most important optimization and operational decision tasks in power systems. This study compares various deterministic mathematical optimization methods, including Lambda Iteration, Newton’s Method, and Quadratic Programming, to solve the ELD problem. The analysis considers both the traditional methodology and a proposed approach that includes turning off less efficient generators to reduce costs and improve system efficiency.
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    Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms
    (IntechOpen, 2018) NASCIMENTO, Manoel Henrique Reis; LEITE, Jandecy Cabral
    The chapter addresses the economic emission load dispatch problem, focusing on minimizing emission levels and total generation cost in thermal power plants. Various multi-objective optimization techniques, including heuristic and metaheuristic algorithms such as Simulated Annealing, Ant Lion, Dragonfly, NSGA II, and Differential Evolution, are analyzed. The chapter also compares the effectiveness of these approaches through a case study applied to a thermal generation plant.
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    Maintenance Management with Application of Computational Intelligence Generating a Decision Support System for the Load Dispatch in Power Plants
    (IntechOpen, 2019) LEITE, Jandecy Cabral; NASCIMENTO, Manoel Henrique Reis
    This chapter proposes the development of a computational tool to support load dispatch decisions based on the operational conditions of motors and generators in thermal power plants. The tool uses a fuzzy system to classify failure probabilities, based on indicators such as lubricating oil analysis, vibration analysis, and thermography of power generation equipment. The goal is not only to monitor the equipment's condition but also to take corrective actions to maintain service reliability and quality, considering the operating conditions of the equipment.