Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell

Chaos Game Optimization Algorithm for Parameters Identification of Different Models of Photovoltaic Solar Cell and Module

In order to achieve the optimum feasible efficiency, the electrical parameters of the photovoltaic solar cell and module should always be thoroughly researched. In reality, the quality of PV designs can have a significant impact on PV system dynamic modeling and optimization. PV models and calculated parameters, on the other hand, have a major effect on MPPT and production system efficiency. Because a solar cell is represented as the most significant component of a PV system, it should be precisely modeled. For determining the parameters of solar PV modules and cells, the Chaos Game Optimization (CGO) method has been presented for the Single Diode Model (SDM). A set of the measured I-V data has been considered for the studied PV design and applied to model the RTC France cell, and Photowatt-PWP201 module. The objective function in this paper is the Root Mean Square Error (RMSE) between the measured and identified datasets of the proposed algorithm. The optimal results that have been obtained by the CGO algorithm for five electrical parameters of PV cell and model have been compared with published results of various optimization algorithms mentioned in the literature on the same PV systems. The comparison proved that the CGO algorithm was superior

8-Parameter extraction in photovoltaic cell using firefly optimization technique

Abstract: Photovoltaic (PV) cell modeling is an important study done to improve solar cell performance before fabrication. Different techniques have been implemented for the extraction of solar cell parameters to generate a high PV power. However, most of these techniques are considered less accurate and suffer some limitations that reduce their effectiveness. In this paper, five different techniques were compared under different cell temperature levels to determine the technique that yields the best results. Findings show that firefly algorithm exhibited the best performance and can be recommended for the extraction of solar cell parameters in PV cells

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Modelação e Análise de Sistemas de Geração Fotovoltaica

A procura de novos recursos energéticos é uma tarefa crucial hoje em dia, já que o mundo está em constante mudança e as sociedades exigem energia para continuar a crescer e a viver. Nesse sentido, a investigação sobre o uso da energia solar tem crescido a cada ano, uma vez que se trata de uma fonte renovável. Particularmente, a produção de energia elétrica através de sistemas solares fotovoltaicos tem aumentado rapidamente na presente década. O que leva à necessidade de prever e analisar o comportamento real dos sistemas solares fotovoltaicos quando em funcionamento. Sendo, para isso necessário, enfrentar o problema desafiador de determinar os parâmetros dos modelos matemáticos das células e módulos fotovoltaicos. O objetivo desta dissertação consiste em determinar os parâmetros fotovoltaicos ótimos através de algoritmos de otimização bio inspirados. Assim como, caraterizar o comportamento das células e módulos fotovoltaicos em diferentes condições de funcionamento. Deste modo, é apresentada uma visão geral da energia solar fotovoltaica e dos seus sistemas de aproveitamento, bem como dos aspetos relacionados com a modelação matemática que permite caraterizar esses sistemas. Particularmente, na modelação matemática são abordados os vários modelos matemáticos e ainda os vários métodos para determinar os parâmetros fotovoltaicos, quer seja a partir da informação disponibilizada pelos fabricantes ou a partir de dados experimentais. No entanto, essa caraterização depende fortemente do valor dos parâmetros fotovoltaicos ótimos, os quais por sua vez dependem das condições de funcionamento. No sentido, de analisar a influência dos parâmetros, é realizada uma variação quantitativa dos parâmetros. Assim como, para analisar a sua dependência (em relação a irradiância e temperatura) é realizado um estudo da variação dos parâmetros fotovoltaicos com as condições de funcionamento. Neste seguimento, e com o objetivo de determinar os parâmetros ótimos dos modelos matemáticos, que caraterizam as células e módulos fotovoltaicos, são propostos novos métodos para determinar os respetivos parâmetros com base em algoritmos de otimização bio inspirados. Por último, é apresentado um novo modelo matemático que permite determinar os parâmetros fotovoltaicos em várias condições de funcionamento e tecnologias fotovoltaicas.The search for new energy resources is a crucial task nowadays, since the world is constantly changing and society’s energy demand to continue to grow and live. In this sense, research on the use of solar energy has been growing each year, since it is a renewable energy source. Particularly, the production of electric energy through photovoltaic solar systems has increased rapidly in this present decade. Which leads to the need to analyze and predict the real operating behavior of photovoltaic solar systems. Being for this purpose necessary, to deal with the challenging problem of determining the mathematical models parameters both for cells and PV modules. The goal of this dissertation is to determine the optimal photovoltaic parameters through optimization bio-inspired algorithms. As well as the cells and photovoltaic modules behavior characterization under different operating conditions. Therefore, it is presented a background overview of photovoltaic solar energy and its utility systems, as well as aspects related to mathematical modeling that allow the characterization of these systems. In particular, regarding mathematical modeling are addressed the various mathematical models and also the existent methods to determine the photovoltaic parameters, either from the information provided by manufacturers or from experimental data. However, this characterization is heavily dependent on the photovoltaic parameters values, which in turn depend on the operating conditions. In order to examine the influence of these parameters, a quantitative variation of those is performed. Furthermore, to analyze its dependency (in relation to irradiance and temperature), a study of the variation of the photovoltaic parameters within operating conditions is conducted. As a follow-up, and with the purpose to determine the optimum parameters of the mathematical models that characterize the cells and photovoltaic modules, new methods based on bio-inspired optimization algorithms are proposed. Finally, a new mathematical model that allows to determines the photovoltaic parameters in several operating conditions and photovoltaic technologies

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Dynamics of Macrosystems; Proceedings of a Workshop, September 3-7, 1984

There is an increasing awareness of the important and persuasive role that instability and random, chaotic motion play in the dynamics of macrosystems. Further research in the field should aim at providing useful tools, and therefore the motivation should come from important questions arising in specific macrosystems. Such systems include biochemical networks, genetic mechanisms, biological communities, neutral networks, cognitive processes and economic structures. This list may seem heterogeneous, but there are similarities between evolution in the different fields. It is not surprising that mathematical methods devised in one field can also be used to describe the dynamics of another. IIASA is attempting to make progress in this direction. With this aim in view this workshop was held at Laxenburg over the period 3-7 September 1984. These Proceedings cover a broad canvas, ranging from specific biological and economic problems to general aspects of dynamical systems and evolutionary theory

Complex and Adaptive Dynamical Systems: A Primer

An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.Comment: unformatted version of the textbook; published in Springer, Complexity Series (2008, second edition 2010

Spéciation guidée par l'environnement‎ : interactions sur des périodes évolutionnaires de communautés de plantes artificielles

Depuis des décades, les chercheurs en Vie Artificielle on créé une pléthore de créatures en utilisant de multiples schémas d’encodage, capacités motrices et aptitudes cognitives. Un motif récurrent, cependant, est que la focalisation est centrée sur les individus à évoluer, ne laissant que peu de place aux variations environnementales. Dans ce travail, nous argumentons que des contraintes abiotiques plus complexes pourraient diriger un processus évolutionnaire vers des régions de l’espace génétique plus robustes and diverses. Nous avons conçu un modèle morphologique complexe, basé sur les graphes orientés de K. Sims, qui repose sur le moteur physique Bullet pour la précision et utilise des contraintes à 6 Degrés de Liberté pour connecter les paires d’organes. Nous avons ainsi évolué un panel de plantes à l’aspect naturel qui devaient survivre malgré des niveaux de ressources variables induits par une source de lumière mobile et des motifs de pluies saisonnières. En plus de cette expérience, nous avons aussi obtenu une meilleure croissance verticale en ajoutant une contrainte biotique artificielle sous la forme de brins d’herbe statiques. La complexité de ce modèle, cependant, ne permettait pas la mise a l’échelle d’une évolution de populations et a donc été réduit dans l’expérience suivante, notamment en supprimant le moteur physique. Cela nous a amené à l’exploration de la co-évolution de populations composées d’une unique espèce et ayant la capacité de se reproduire de manière autonome grâce à notre Bail-Out Crossover (Croisement avec Désistement). Bien que les populations résultantes n’ont pas démontré un grand intérêt pour cette aptitude, elles ont néanmoins fourni d’importantes informations sur les mécanismes d’auto-reproduction. Ceux-ci ont été mis en action dans un second modèle inspiré des travaux de Bornhofen. Grâce à sa légèreté, cela nous a permis de traiter non seulement de plus grandes populations (de l’ordre de milliers d’individus) mais aussi de plus longues périodes évolutionnaires (100 années, approximativement 5000 générations). Notre première expérience avec ce modèle s’est concentrée sur la possibilité de reproduire des cas d’école de spéciation (allopatrique, parapatrique, péripatrique) sur cette plate-forme. Grâce à APOGet, une nouvelle procédure de regroupement pour l’extraction en parallèle d’espèces à partir d’un arbre généalogique, nous avons pu affirmer que le système était effectivement capable de spéciation spontanée. Cela nous a conduit à une dernière expérience dans laquelle l’environnement était contrôlé par de la Programmation Génétique Cartésienne (CGP), permettant ainsi une évolution automatique d’une population et des contraintes abiotiques auxquelles elle était confrontée. Par une variation du traditionnel algorithme 1 + λ nous avons obtenu 10 populations finales qui ont survécu à de brutales et imprévisibles variations environnementales. En les comparant à un groupe contrôle c pour lequel les contraintes ont été maintenues faibles et constantes, le groupe évolué e a montré des performances mitigées: dans les deux types de tests, une moitié de e surpassait c qui, à son tour, surpassait la moitié restante de e. Nous avons aussi trouvé une très forte corrélation entre les chutes catastrophiques de population et la performance des évolutions correspondantes. Il en résulte que l’évolution de population dans des environnements hostiles et dynamiques n’est pas une panacée bien que ces expériences en démontrent le potentiel et souligne le besoin d’études ultérieures plus approfondies.Artificial Life researchers have, for decades, created a plethora of creatures using numerous encoding schemes, motile capabilities and cognitive capacities. One recurring pattern, however, is that focus is solely put on the evolved individuals, with very limited environmental variations. In this work, we argue that more complex abiotic constraints could drive an evolutionary process towards more robust and diverse regions of the genetic space. We started with a complex morphogenetic model, based on K. Sims’ directed graphs, which relied on the Bullet physics engine for accuracy and used 6Degrees of Freedom constraints to connect pairs of organs. We evolved a panel of natural-looking plants which had to cope with varying resource levels thanks to a mobile light source and seasonal rain patterns. In addition to this experiment, we also obtained improved vertical growth by adding an artificialbiotic constraint in the form of static grass blades. However, the computational cost of this model precluded scaling to a population-level evolution and was reduced in the successive experiment, notably by removing the physical engine. This led to the exploration of co-evolution on single-species populations which, thanks to our Bail-Out Crossover (BOC) algorithm, were able to self-reproduce. The resulting populations provided valuable insight into the mechanisms of self-sustainability. These were put to action in an even more straightforward morphogenetic model inspired by the work of Bornhofen. Due to its light weightness, this allowed for both larger populations (up to thousands of individuals) and longer evolutionary periods (100 years, roughly 5K generations). Our first experiment on this model tested whether text-book cases of speciation could be reproduced in our framework. Such positive results were observed thanks to the species monitoring capacities of APOGeT, a novel clustering procedure we designed for online extraction of species from a genealogic tree. This drove us to a final experiment in which the environment was controlled through Cartesian Genetic Programming thus allowing the automated evolution of both the population and abiotic constraints it is subjected to. Through a variation of the traditional1 + λ algorithm, we obtained 10 populations (evolved group e) which had endured in harsh and unpredictable environments. These were confronted to a control group c, in which the constraints were kept mild and constant, on two types of colonization evaluation. Results showed that the evolved group was heterogeneous with half of e consistently outperforming members of c and the other half exhibiting worse performances than the baseline. We also found a very strong positive correlation between catastrophic drops in population level during evolution with the robustness of their final representatives. From this work, two conclusions can be drawn. First, though the need to fight on both the abiotic and biotic fronts can lead to worse performances, more robust individuals can be found in reasonable time-frames. Second, the automated co-evolution of populations and their environments is essential in exploring counter-intuitive, yet fundamental, dynamics both in biological and artificial life