Gerenciamento da climatização de células a combustível do tipo PEM para integração com a rede de energia elétrica visando à eficiência energética

• Ano de defesa: 2010
• Autor(a) principal: Ramos, Diego Berlezi
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Engenharia Elétrica; UFSM; Programa de Pós-Graduação em Engenharia Elétrica; Centro de Tecnologia
• Palavras-chave em Português: Eficiência de células a combustível; Membrana de troca protônica; Controle fuzzy; Modelagem ANFIS; Ilhamento; Fuel cells efficiency; Proton exchange membrane; Fuzzy logic control; ANFIS modeling; Islanding; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.ufsm.br/handle/1/27332

Fuel cells (FCs) offer a friendly environmental way to generate energy. Specifically, these devices require only a fuel (generally, hydrogen) and an oxidizer (pure oxygen or air) to produce electricity, water and heat. It is a result of the electrochemical reactions occurring in a polymeric solid membrane. The Proton Exchange Membrane Fuel Cells (PEMFCs) are calling attention by special characteristics: low operational temperature, simplicity, modularity, portability and efficiency. However, its energetic performance is highly susceptible to external influences: reactants pressure (or flux), operational temperature, membrane water content, weather and load conditions. This sensitivity makes the FC efficiency highly variable. Thus, this thesis proposes a FC management within specific efficient operating conditions. It is accomplished by simultaneous coordinating the main variables affecting generator’s efficiency: temperature, pressure and humidity. The proposed algorithm maintains the FC working within its linear operating region. Here, the losses are limited by rational fuel utilization. Considering the intrinsic and relative complexity of the relationships to stabilize the PEMFC membrane electrode assembly (MEA) this thesis uses intelligent algorithms, such as neural networks and fuzzy logic to emulate the FC behavior and to promote an efficient operation. These effects are checked with practical results obtained with simultaneous occurrence of the above mentioned external factors. The isolated treatment of these variables does not allow to reach higher efficiency levels. With the obtained data analysis it is possible to develop a control algorithm by using a neuro-fuzzy inference system (ANFIS) to simplify the FC modeling task. One of the main FC generator applications relies on the wide range of power source integration. Also, it is considered the main grid connection, assuming that the generator is subjected to load variations as a result of islanding events. It is a critical situation, mainly when there is load sharing between the local generator and the main grid, which is used as a reference to justify the control algorithm ability to track the operational FC efficiency after any load variation. FC simulated and practical results show that it is possible to reach ranges between 5 to 20% of higher efficiency levels without adversely affecting fuel consumption and membrane integrity.