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IEEE Conference on Electromagnetic Field Computation

作者： Helon Vicente Hultmann Ayala Carlos Eduardo Klein Viviana Cocco Mariani Leandro dos Santos Coelho Pontificia Universidade Catolica do Parana Curitiba PR BR Pontifical Catholic University of Parana (PUCPR) Industrial and Systems Engineering Graduate Program (PPGEPS) Curitiba PR Brazil Pontifical Catholic University of Parana (PUCPR) Mechanical Engineering Graduate Program (PPGEM) Curitiba PR Brazil Department of Electrical Engineering Federal University of Parana Curitiba PR Brazil

ISBN: (纸本)9781509010332

Optimization metaheuristics are a powerful way to deal with many electromagnetic optimization problems. Recently, the symbiotic organisms search (SOS) algorithm was proposed to solve single-objective optimization problems. SOS mimics the symbiotic relationship among the living beings. In order to extend the classical mono-objective SOS algorithm approach, this paper proposes a new multi-objective SOS (MOSOS) and an improved IMOSOS. Results on a multi-objective constrained brushless direct current (DC) motor design show that the MOSOS and IMOSOS present promising performance.

关键词： Optimization Brushless DC motors Symbiosis Algorithm design and analysis Organisms

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Multiobjective cross entropy for electromagnetic optimization

Multiobjective cross entropy for electromagnetic optimizatio...

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IEEE Conference on Electromagnetic Field Computation

作者： Teodora Cardoso Bora Viviana Cocco Mariani Leandro dos Santos Coelho Piergiorgio Alotto Department of Electrical Engineering Federal University of Parana Curitiba PR Brazil Pontifical Catholic University of Parna (PUCPR) Mechanical Engineering Graduate-Program (PPGEM) Curitiba PR Brazil Pontifical Catholic University of Parana (PUCPR) Industrial and Systems Engineering Graduate Program (PPGEPS) Curitiba PR Brazil Dipartimento di Ingegneria Industriale Università di Padova Padua Italy

ISBN: (纸本)9781509010332

The Cross-Entropy (CE) method has been designed to solve rare event simulation problems and later extended to single-objective optimization problems. In this paper, we propose a novel CE approach for multi-objective optimization and applied it to the multiobjective version of TEAM (Testing Electromagnetic Analysis Method) workshop problem 22, demonstrating promising results in terms of performance.

关键词： Optimization Conferences Monte Carlo methods Entropy Testing Electromagnetic analysis Histograms

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Forecasting electricity prices using a RBF neural network With GARCH errors

Forecasting electricity prices using a RBF neural network Wi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Andre Alves Portela Santos Leandro dos Santos Coelho Carlos Eduardo Klein Department of Statistics Universidad Carlos III de Madrid Spain Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil CNPq National Council for Scientific and Technological Development Brazil

In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose a novel estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide hour-ahead point and direction-of-change forecasts of the Spanish electricity pool prices.

关键词： Load modeling Artificial neural networks Predictive models

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Control and Tracking Techniques for Switched Reluctance Machines 1

Control and Tracking Techniques for Switched Reluctance Mach...

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丛书名： Power systems

1000年

作者： Wesley Pacheco Calixto Wanderson Rainer Hilário Araújo Lucas Diniz Silva Morais Marcio Rodrigues Cunha Reis

ISBN: (数字)9783031867279

ISBN: (纸本)9783031867262;9783031867293

Control and Tracking Techniques for Switched Reluctance Machines provides detailed and practical instructions for implementing drive and control techniques for switched reluctance machines (SRMs), which can be immediately applied in real-world projects. It presents the latest innovations in control techniques for SRMs, which are essential for the efficiency and sustainability of modern electrical systems. The book includes case studies and practical examples that enhance the understanding of concepts and their application in real scenarios, making the content accessible to both students and experienced professionals. It emphasizes techniques that optimize SRM performance and promote the sustainability of electrical systems, a topic of increasing importance in engineering. With a focus on the current and future needs of the energy sector, this authoritative guide is a key reference for practicing engineers, researchers, and practitioners in the renewable energy industry. Presents the latest innovations in control techniques for switched reluctance machines; Emphasizes techniques and innovation with a focus on sustainability; Offers case studies and a practical approach allowing immediate technology applications in real-world projects.

关键词： Power Electronics, Electrical Machines and Networks Energy systems Renewable and Green Energy

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Photovoltaic Generation Potential of Paraná State, Brazil – A Comparative Analysis with European Countries

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Energy Procedia 2014年 57卷 725-734页

作者： Gerson Maximo Tiepolo Jair Urbanetz Junior Osiris Canciglieri Junior Trajano Viana Department of Electrical Engineering Technological Federal University of Paraná – UTFPR Avenida Sete de Setembro 3165 80230-901 Curitiba Paraná Brazil Graduate Program in Production Engineering and Systems – PPGEPS Pontifical Catholic University of Parana - PUCPR Rua Imaculada Conceição 115 80215-901 Curitiba Paraná Brazil Federal Center of Technological Education - CEFET/RJ Avenida Maracanã 229 20271-110 Rio de Janeiro Brazil

The theme of sustainable development has increasingly impacted the spheres of Brazilian society and has contributed to greater awareness to the need of natural resources conservation as well as issues on how to continue promoting socio-economic development of regions. One of the key issues for sustainable development is the generation of electricity with more use of renewable sources and, therefore, less environmental degradation. In this scenario, historically, Paraná State of Brazil has been one of the largest producers of electricity in the country, almost entirely from hydraulic source, due to the large existing river basin in the state. However, the use of this source in Brazil is declining due to depletion of water potential and also the pressure of society with respect to environmental, social and economic impacts caused by damming of rivers and flooding of cities and areas to form large reservoirs. To overcome these limitations, other sources have been studied and used, such as biomass and wind. Photovoltaic generation has been contemplated in Brazilian government policies only to supply electricity with off-grid systems in remote locations. However, due to the recent Resolution 482/2012 of ANEEL (Brazilian Electricity Regulatory Agency), which establishes rules for micro and mini-generation of electricity, it is possible that consumers install grid connected photovoltaic systems up to 1 MWp, as have been widely used in the urban environment installed or integrated on roofs as a form of distributed generation, particularly in Europe, where counts with installed capacity in GW range, while in Brazil it counts only with a few MW. Given this new perspective of growth of the Brazilian photovoltaic electricity generation this paper presents the potential of solar photovoltaic power generation in Paraná State, Brazil, with a comparative analysis with the potential presented by some European countries. The presented solar and photovoltaic maps uses irradiation data

关键词： Sustainable Development Renewable Energy Photovoltaic Generation Distributed Generation Public Policy

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Evaluation of Interoperability between Automation systems using Multi-criteria Methods

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Procedia Manufacturing 2017年 11卷 1837-1845页

作者： Saturno, Maicon Ramos, Luiz Felipe Pierin Polato, Fabrício Deschamps, Fernando de Freitas Rocha Loures, Eduardo Graduate Program in Industrial and Systems Engineering (PPGEPS) Polytechnic School Pontifical Catholic University of Parana (PUCPR) Rua Imaculada Conceição Curitiba 1155 Paraná Brazil Dominus – Automação Sistemas e Acionamentos Avenida Manoel Ribas 8.120 Curitiba Paraná Brazil Department of Mechanical Engineering (DEMEC) Federal University of Paraná (UFPR) Rua Coronel Francisco Heráclito dos Santos Curitiba 230 Paraná Brazil Department of Electro-tecnology (DAEL) Federal University of Technology – Paraná (UTFPR) Avenida Sete de Setembro Curitiba 3033 Paraná Brazil

The diagnosis of automation systems in an existing production system through the analysis of the interoperability level between its components represents the first step in its evaluation for improving it towards alignment with Industry 4.0. The analysis of the level of interoperability finds in the multi-criteria decision making methods an auxiliary tool to evaluate and classify the solution. This work uses the AHP method for this evaluation, drawing criteria from the literature and expert experience. The application of the method is illustrated, showing that it can direct investment decisions. © 2017 The Authors

关键词： automation systems Automation technology (AT) diagnosis interoperability multi-criteria methods

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On the Improvement of Elite Swimmers Velocity Identification by Using Neural Network Associated to Multiobjective Optimization

On the Improvement of Elite Swimmers Velocity Identification...

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International Conference on Artificial Intelligence, Modelling and Simulation (AIMS)

作者： Elcio A. Bardeli Luciano F. Da Cruz Helon V.H. Ayala Roberto Z. Freire Leandro dos S. Coelho Industrial and Systems Engineering Graduate Program - PPGEPS University of Parana - PUCPR Curitiba Brazil Polytech. Sch. Ind. & Syst. Eng. Grad. Pontificia Universidade Catolica do Parana Curitiba PR BR Electrical Engineering Graduate Program - PPGEE Polytechnic School of the Federal University of Parana - UFPR Curitiba Brazil

Considering that technical skill is the major determinant characteristic of success among competitive swimmers, it is important to coaches to quantify the differences that make one swimmer more efficient than another. One of the most important grants in swimming is the velocity, which can be related to drag forces and provide substantial information about the swimmer technique. The main purpose of this study was to determine the best model to compare swimmers in terms of velocity. In this work, a Radial Basis Function Neural Network (RBF-NN) was used to model the nonlinearity of swim velocity time series. The RBF-NN parameters were adjusted by using four multiobjective optimization methods. The best results in terms of RBF-NN configuration were obtained by the Differential Evolution based algorithms.

关键词： Sociology Optimization methods Time series analysis Measurement Sorting

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Multivariable nonlinear boiler power plant identification through neural networks and Particle Swarm Optimization approaches

Multivariable nonlinear boiler power plant identification th...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Fábio A. Guerra Helon V. H. Ayala André E. Lazzaretti Marcio R. Sans Leandro S. Coelho Cesar A. Tacla Eletroelectronics Department (DPEE) Electrical System Division (DVSE) Centro Politécnico da UFPR LACTEC-Institute of Technology for Development Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program (LAS/PPGEPS) Pontifical Catholic University of Paraná Curitiba Parana Brazil Graduate Program in Eletrical Engineering and Industrial Informatics (CPGEI) Federal University of Technology Parana Curitiba Parana Brazil

The identification of nonlinear systems with artificial neural networks models has been successfully used in many applications. Most processes in industry are characterized by nonlinear and time-varying behavior. In this context, the identification of mathematical models for nonlinear systems is vital in many fields of engineering. The Radial Basis Function Neural Network (RBF-NN) is a powerful approach for nonlinear identification and can be improved using Particle Swarm Optimization (PSO) approaches. This paper presents a multivariable nonlinear system identification using RBF-NN combined with standard PSO and Constriction Factor PSO (CFPSO) approaches in order to determine the RBF-NN parameters. RBF-NN is considered to be a good choice for black-box modeling problems due to its rapid learning capacity and, therefore, has been applied successfully to nonlinear time series modeling and nonlinear identification. On the other hand, PSO was inspired by the choreography of bird flocks and fish schools and can be seen as a distributed behavior algorithm that performs multidimensional search. Furthermore, promising simulation results from performance analysis of the proposed RBF-NN with PSO training approaches are presented and discussed in this paper showing promising results.

关键词： Artificial neural networks Boilers Neurons Modeling Power generation Optimization Nonlinear systems

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Opposition-based shuffled PSO with passive congregation applied to FM matching synthesis

Opposition-based shuffled PSO with passive congregation appl...

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Congress on Evolutionary Computation

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia Brazil

Synthesis of musical instruments or human voice is a time consuming process which requires theoretical and experimental knowledge about the synthesis engine. Commonly, performers need to deal with synthesizer interfaces and a process of trial and error for creating musical sounds similar to a target sound. This drawback can be overcome by adjusting automatically the synthesizer parameters using optimization algorithms. In this paper a hybrid particle swarm optimization (PSO) algorithm is proposed to solve the frequency modulation (FM) matching synthesis problem. The proposed algorithm takes advantage of a shuffle process for exchanging information between particles and applies the selective passive congregation and the opposition-based learning approaches to preserve swarm diversity. Both approaches for injecting diversity are based on simple operators, preserving the easy implementation philosophy of the particle swarm optimization. The proposed hybrid particle swarm optimization algorithm was validated for a three-nested FM synthesizer, which represents a 6-dimensional multimodal optimization problem with strong epistasis. Simulation results revealed that the proposed algorithm presented promising results in terms of quality of solutions.

关键词： Frequency modulation Optimization Equations Convergence Particle swarm optimization Genetic algorithms

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Multi-step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods

Multi-step ahead Bitcoin Price Forecasting Based on VMD and ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ramon Gomes da Silva Matheus Henrique Dal Molin Ribeiro Naylene Fraccanabbia Viviana Cocco Mariani Leandro dos Santos Coelho Industrial & Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Electrical Engineering Federal University of Parana (UFPR) Curitiba Parana Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bitcoin is the leading currency in the cryptocurrency market capturing attention worldwide. Forecasting the Bitcoin price as accurate as possible is essential, but due to its high volatility this task is challenging. Many researchers try, through the years, to develop efficient models for predicting the Bitcoin price using several different data-driven approaches. The objective of this paper is to develop a novel decomposition-ensemble learning model that combines Variational Mode Decomposition (VMD) and Stacking-ensemble learning (STACK) with machine learning algorithms to forecast the Bitcoin price multi-step ahead. The algorithms are k-Nearest Neighbors, Support Vector Regression with Linear kernel, Feed-forward Artificial Neural Network with single-layer perceptron, Generalized Linear Model, and Cubist. Correlation matrix (CORR), principal component analysis (PCA), and Box-Cox transformation (BOXCOX) were used as data preprocessing techniques. Estimating the performance of the proposed models (namely VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) using relative root mean square error, symmetric mean absolute percentage error, and absolute percentage error measures, defined that for one-day-ahead forecast VMD-STAK-BOXCOX model presented the better performance, and for two and three-days-ahead forecast VMD-STACK-CORR model was chosen, compared to VMD, STACK, and machine learning algorithms models' performance. Diebold-Mariano statistical test was conducted to evaluate a reduction in forecasting errors. Therefore, the proposed models (VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) indeed forecast accurately Bitcoin price and outperformed the compared models (VMD, STACK, and machine learning models).

关键词： Predictive models Biological system modeling Data models Bitcoin Training Machine learning algorithms

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