Livros e capítulo

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

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Resultados da Pesquisa

Agora exibindo 1 - 3 de 3
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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.
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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, 2023) NASCIMENTO, Manoel Henrique Reis; ALENCAR, David Barbosa de
    This chapter discusses different deterministic optimization methods applied to economic load dispatch, focusing on switching off less efficient generators. The study investigates the efficiency of methods such as linear programming, quadratic programming, and other mathematical approaches to reduce operational costs and improve energy efficiency in power generation systems. The research highlights how optimization can contribute to more cost-effective and environmentally friendly decision-making, proposing strategies to integrate these methodologies into power generation systems.