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Um novo algoritmo de MPPT híbrido baseado nos algoritmos Particle Swarm Optimization e Perturba e Observa, assistidos pela técnica de interpolação Spline Cúbica
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Abstract(s)
A utilização de combustíveis fósseis tem vindo a diminuir ao longo dos últimos anos
devido principalmente à menor disponibilidade destes recursos e ao aumento da
preocupação ambiental. A diminuição das reservas de combustíveis fósseis e a recente
invasão da Ucrânia pela Rússia provocaram um aumento nos preços dos combustíveis
fósseis, levando a União Europeia a acelerar a produção de energia através de outras
fontes. Além disso, a queima desses combustíveis fósseis liberta grandes quantidades de
gases com efeito de estufa, sendo estes os principais responsáveis pelo aquecimento
global. Por estes motivos, a produção de energia elétrica através de fontes renováveis tem
estado constantemente a aumentar, contribuindo para a urgente redução da pegada de
carbono da sociedade.
Das várias fontes de energia renováveis existentes, a produção de energia elétrica
através de sistemas fotovoltaicos é a que tem apresentado o maior crescimento, que se
deve principalmente à facilidade de implementação dos sistemas fotovoltaicos e à
possibilidade de serem aplicados para uso doméstico em meios urbanos. O aumento da
procura pela energia solar faz com que os sistemas fotovoltaicos tenham de ser cada vez
mais eficientes, rápidos, precisos e confiáveis, de forma a aproveitar o máximo de energia
elétrica disponível em cada momento. No entanto, o bom funcionamento desses sistemas
fotovoltaicos deve ser acompanhado de uma implementação simples, continuando a
permitir a aplicação para uso doméstico.
O objetivo da presente dissertação é propor um novo algoritmo de procura do ponto
de máxima potência em sistemas fotovoltaicos. O novo algoritmo proposto combina as
vantagens de dois algoritmos de procura do ponto de máxima potência (o algoritmo
convencional Perturba e Observa e o algoritmo avançado meta-heurístico Particle
Swarm Optimization) com uma técnica de interpolação (Spline Cúbica) que permite
estimar as curvas características corrente-tensão e potência-tensão do sistema
fotovoltaico. Para selecionar a técnica Spline Cúbica como a técnica de interpolação ideal
para utilizar no algoritmo proposto, várias técnicas de interpolação foram aplicadas a
três casos de estudo, permitindo os resultados escolher a técnica mais adequada ao
problema proposto.
O funcionamento do algoritmo proposto na presente dissertação inicia-se com
algoritmo Particle Swarm Optimization, que procura os vinte melhores pontos para
serem utilizados na interpolação. De seguida, esses pontos são utilizados na interpolação
de Spline Cúbica, permitindo obter as curvas características corrente-tensão e potênciatensão nesse momento de funcionamento. Posteriormente, através da curva potênciatensão estimada durante a interpolação, é determinado o ponto de máxima potência
estimado. A partir desse ponto inicia-se o algoritmo Perturba e Observa que, através da técnica de tentativa e erro, fornece uma melhor aproximação do ponto de máxima
potência do sistema fotovoltaico.
Para validar o algoritmo proposto, o mesmo foi utilizado em dois casos de estudo
simulados com diferentes condições de funcionamento. O desempenho do algoritmo
proposto foi comparado com o algoritmo de procura do ponto de máxima potência
convencional Perturba e Observa e com os algoritmos de procura do ponto de máxima
potência meta-heurísticos Particle Swarm Optimization, Grey Wolf Optimizer e
Differential Evolution. A análise dos resultados permite concluir que o algoritmo
proposto na presente dissertação no geral superou os restantes algoritmos utilizados,
apresentando melhores valores de taxa de sucesso, tempo de convergência, número de
avaliações necessárias para convergir, proximidade ao ponto de máxima potência global
e eficiência. O algoritmo proposto apresenta-se assim como um algoritmo robusto e
preparado para funcionar em quaisquer condições de funcionamento.
The use of fossil fuels has been declining in recent years, mainly due to the reduced availability of these resources and increased environmental concerns. The dwindling fossil fuel reserves and the recent Russian invasion of Ukraine have led to an increase in fossil fuel prices, prompting the European Union to accelerate energy production from other sources. In addition, the burning of these fossil fuels releases large amounts of greenhouse gases, which are the main contributors to global warming. For these reasons, the production of electricity from renewable sources has been steadily increasing, contributing to the urgent reduction of society's carbon footprint. Of the various renewable energy sources available, electricity production has shown the greatest growth, mainly due to the ease of implementing photovoltaic systems and the possibility of their application for domestic use in urban areas. The increase in demand for solar energy means that photovoltaic systems must be increasingly efficient, fast, precise and reliable, to make the most of the electrical energy available at any given time. However, the proper functioning of these photovoltaic systems must be accompanied by simple implementation, while still allowing their application for domestic use. The objective of this dissertation is to propose a new algorithm for finding the maximum power point in photovoltaic systems. The new proposed algorithm combines the advantages of two algorithms for finding the maximum power point (the conventional Perturb and Observe algorithm and the advanced meta-heuristic Particle Swarm Optimization algorithm) with an interpolation technique (Cubic Spline) that allows estimating the current-voltage and power-voltage characteristic curves of the photovoltaic system. To select the Cubic Spline technique as the optimal interpolation technique to be used in the proposed algorithm, several interpolation techniques were applied to three case studies, allowing the results to choose the most appropriate technique for the proposed problem. The operation of the algorithm proposed in this dissertation begins with the Particle Swarm Optimization algorithm, which searches for the twenty best points to be used in the interpolation. These points are then used in the Cubic Spline interpolation, allowing to obtain the current-voltage and power-voltage characteristic curves at that moment of operation. Subsequently, through the power-voltage curve estimated during the interpolation, the point of maximum power is determined. To validate the proposed algorithm, it was used in two simulated case studies with different operating conditions. The performance of the proposed algorithm was compared with the conventional maximum power point search algorithm Perturb and Observe and with the metaheuristic maximum power point search algorithms Particle Swarm Optimization, Grey Wolf Optimizer and Differential Evolution. The analysis of the results allows us to conclude that the algorithm proposed in this dissertation generally outperformed the other algorithms used, presenting better values of success rate, convergence time, number of evaluations required to converge, proximity to the global maximum power point and efficiency. The proposed algorithm is thus presented as a robust algorithm prepared to operate under any operating conditions.
The use of fossil fuels has been declining in recent years, mainly due to the reduced availability of these resources and increased environmental concerns. The dwindling fossil fuel reserves and the recent Russian invasion of Ukraine have led to an increase in fossil fuel prices, prompting the European Union to accelerate energy production from other sources. In addition, the burning of these fossil fuels releases large amounts of greenhouse gases, which are the main contributors to global warming. For these reasons, the production of electricity from renewable sources has been steadily increasing, contributing to the urgent reduction of society's carbon footprint. Of the various renewable energy sources available, electricity production has shown the greatest growth, mainly due to the ease of implementing photovoltaic systems and the possibility of their application for domestic use in urban areas. The increase in demand for solar energy means that photovoltaic systems must be increasingly efficient, fast, precise and reliable, to make the most of the electrical energy available at any given time. However, the proper functioning of these photovoltaic systems must be accompanied by simple implementation, while still allowing their application for domestic use. The objective of this dissertation is to propose a new algorithm for finding the maximum power point in photovoltaic systems. The new proposed algorithm combines the advantages of two algorithms for finding the maximum power point (the conventional Perturb and Observe algorithm and the advanced meta-heuristic Particle Swarm Optimization algorithm) with an interpolation technique (Cubic Spline) that allows estimating the current-voltage and power-voltage characteristic curves of the photovoltaic system. To select the Cubic Spline technique as the optimal interpolation technique to be used in the proposed algorithm, several interpolation techniques were applied to three case studies, allowing the results to choose the most appropriate technique for the proposed problem. The operation of the algorithm proposed in this dissertation begins with the Particle Swarm Optimization algorithm, which searches for the twenty best points to be used in the interpolation. These points are then used in the Cubic Spline interpolation, allowing to obtain the current-voltage and power-voltage characteristic curves at that moment of operation. Subsequently, through the power-voltage curve estimated during the interpolation, the point of maximum power is determined. To validate the proposed algorithm, it was used in two simulated case studies with different operating conditions. The performance of the proposed algorithm was compared with the conventional maximum power point search algorithm Perturb and Observe and with the metaheuristic maximum power point search algorithms Particle Swarm Optimization, Grey Wolf Optimizer and Differential Evolution. The analysis of the results allows us to conclude that the algorithm proposed in this dissertation generally outperformed the other algorithms used, presenting better values of success rate, convergence time, number of evaluations required to converge, proximity to the global maximum power point and efficiency. The proposed algorithm is thus presented as a robust algorithm prepared to operate under any operating conditions.
Description
Keywords
Energia Fotovoltaica Interpolação de Spline
Cúbica Particle Swarm Optimization Perturba e Observa Procura do Ponto de Máxima Potência