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Desenvolvimento e aplicação do algoritmo Chaotic Electric Eel Foraging na procura do ponto de máxima potência em sistemas fotovoltaicos
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Authors
Abstract(s)
Estamos profundamente envolvidos numa constante dinâmica de mudança e na
busca por soluções mais eficientes em comparação com as normas pré-estabelecidas, seja
no âmbito tecnológico, energético ou em qualquer outra área existente. Neste contexto,
as energias renováveis desempenham um papel cada vez mais relevante no
funcionamento da sociedade, sendo atualmente a energia fotovoltaica uma das áreas com
maior atividade de investigação. A investigação nesta área revela-se essencial,
especialmente face à crescente emergência de novas tecnologias fotovoltaicas mais
otimizadas e eficientes, exigindo-se uma análise meticulosa e uma compreensão
aprofundada do seu funcionamento. Para concretizar este objetivo, surge o desafio
complexo de propor um novo algoritmo de procura do ponto de máxima potência, com
o intuito de extrair a máxima potência disponível de um sistema fotovoltaico.
O principal objetivo desta dissertação é o desenvolvimento e a implementação de
um algoritmo bio-inspirado e computacionalmente avançado na área fotovoltaica, com
o intuito de explorar o seu potencial na procura do ponto de máxima potência. Este
algoritmo, denominado de Chaotic Electric Eel Foraging Optimization, é uma
modificação do recente algoritmo Electric Eel Foraging Optimization, que, por sua vez,
é inspirado no comportamento de procura exibido pelas enguias elétricas na natureza.
Para testar e validar o algoritmo proposto, foram simulados três casos de estudo:
o primeiro em condições sem sombreamento parcial, o segundo com condições de ligeiro
sombreamento parcial e o terceiro com três situações de condições de sombreamento
parciais mais complexas. O desempenho do algoritmo proposto foi comparado com o
método convencional de procura do ponto de máxima potência mais utilizado, o Perturba
e Observa, bem como com três métodos computacionais avançados de procura do ponto
de máxima potência bio-inspirados, nomeadamente o Flower Pollination, o Grey Wolf
Optimizer e o Particle Swarm Optimization. Os resultados obtidos demonstraram que o
algoritmo proposto superou os restantes em termos de taxa de sucesso, número de
iterações até à convergência e eficiência, mesmo em condições de sombreamento parcial
complexas.
We are deeply engaged in a constant dynamic of change and in the pursuit of more efficient solutions compared to pre-established standards, whether in the technological, energy, or any other existing field. In this context, renewable energies play an increasingly significant role in the functioning of society, with photovoltaic energy currently being one of the most active areas of research. Research in this area is essential, especially in light of the growing emergence of new, more optimized, and efficient photovoltaic technologies, which require a meticulous analysis and a deep understanding of their operation. To achieve this goal, a complex challenge arises in proposing a new maximum power point tracking (MPPT) algorithm, with the aim of extracting the maximum available power from a photovoltaic system. The main objective of this dissertation is the development and implementation of a bio-inspired and computationally advanced algorithm in the photovoltaic field, with the purpose of exploring its potential in the search for the maximum power point. This algorithm, called Chaotic Electric Eel Foraging Optimization, is a modification of the recent Electric Eel Foraging Optimization algorithm, which is itself inspired by the foraging behavior exhibited by electric eels in nature. To test and validate the proposed algorithm, three case studies were simulated: the first under conditions without partial shading, the second with mild partial shading conditions, and the third with three scenarios involving more complex partial shading conditions. The performance of the proposed algorithm was compared to the conventional maximum power point tracking method, Perturb and Observe, as well as to three advanced computational bio-inspired MPPT methods, namely Flower Pollination, Grey Wolf Optimizer, and Particle Swarm Optimization. The results demonstrated that the proposed algorithm outperformed the others in terms of success rate, number of iterations to convergence, and efficiency, even under complex partial shading conditions.
We are deeply engaged in a constant dynamic of change and in the pursuit of more efficient solutions compared to pre-established standards, whether in the technological, energy, or any other existing field. In this context, renewable energies play an increasingly significant role in the functioning of society, with photovoltaic energy currently being one of the most active areas of research. Research in this area is essential, especially in light of the growing emergence of new, more optimized, and efficient photovoltaic technologies, which require a meticulous analysis and a deep understanding of their operation. To achieve this goal, a complex challenge arises in proposing a new maximum power point tracking (MPPT) algorithm, with the aim of extracting the maximum available power from a photovoltaic system. The main objective of this dissertation is the development and implementation of a bio-inspired and computationally advanced algorithm in the photovoltaic field, with the purpose of exploring its potential in the search for the maximum power point. This algorithm, called Chaotic Electric Eel Foraging Optimization, is a modification of the recent Electric Eel Foraging Optimization algorithm, which is itself inspired by the foraging behavior exhibited by electric eels in nature. To test and validate the proposed algorithm, three case studies were simulated: the first under conditions without partial shading, the second with mild partial shading conditions, and the third with three scenarios involving more complex partial shading conditions. The performance of the proposed algorithm was compared to the conventional maximum power point tracking method, Perturb and Observe, as well as to three advanced computational bio-inspired MPPT methods, namely Flower Pollination, Grey Wolf Optimizer, and Particle Swarm Optimization. The results demonstrated that the proposed algorithm outperformed the others in terms of success rate, number of iterations to convergence, and efficiency, even under complex partial shading conditions.
Description
Keywords
Chaotic Electric Eel Foraging Optimization Algoritmos de Procura do Ponto de Máxima Potência Sistema Fotovoltaico