Utilização da lógica fuzzy na geração de zonas de manejo

Este trabalho teve como objetivo utilizar a lógica fuzzy para geração de zonas de manejo, na área agrária e ambiental. Uma das aplicações consistiu da utilização do método fuzzy C-means, para geração de zonas de manejo para a cultura do mamoeiro, em um plantio comercial localizado em São Mateus-ES, com base em determinações realizadas através de amostragens e análises químicas do solo, considerando os atributos: P, K, Ca, Mg, e Saturação por bases (V%). Aplicou-se também a lógica fuzzy para desenvolver e executar um procedimento para dar suporte ao processo de tomada de decisões, envolvendo análise multicritério, gerando mapas de adequabilidade ao uso público e a conservação no Parque Estadual da Cachoeira da Fumaça, no município de Alegre-ES, considerando como fatores a localização da cachoeira, o uso do solo, os recursos hídricos, as trilhas, os locais de acessos, a infraestrutura, a declividade da área, e utilizando a abordagem de Sistema de Informações Geográficas para análise e combinação da base de dados. A partir das zonas de manejo geradas, foi possível explicar a variabilidade espacial dos atributos do solo na área de estudo da cultura do mamoeiro, e observa-se que as similaridades entre as zonas geradas, a partir de diferentes atributos, mostrou variação, mas observa-se uma influência nos dados, principalmente pelos atributos P e V. A partir do zoneamento da Unidade de Conservação foi possível selecionar áreas mais aptas ao ecoturismo, sendo encontradas próximas da cachoeira, trilhas em zonas de reflorestamento e de Mata Atlântica. Quanto às áreas propensas a medidas de conservação localizam-se próximas à cachoeira e às estruturas do parque, devido à maior pressão antrópica exercida nesses locais. Outras áreas que se destacaram, foram as áreas de pastagem, por estarem em estágio de regeneração natural. Os resultados indicam áreas de mesmo potencial de produção do mamoeiro, ou quando aplicado à área ambiental, áreas que devem receber maior cuidado para utilização por ecoturismo e para preservação e servem de base para a tomada de decisões, visando melhor aproveitamento da área.

Um Modelo Fuzzy Comportamental para análise de sobre-reação e sub-reação no mercado de ações brasileiro

Neste artigo, são apresentados testes empíricos para a investigação de ocorrência de fenômenos de sobre-reação e sub-reação no mercado de ações brasileiro. Para esses testes, é proposto um modelo baseado na teoria de conjuntos Fuzzy, que possui forte relação com as heurísticas de representatividade e ancoramento, estabelecidas na teoria de finanças comportamentais. O modelo proposto é empregado para a formação de carteiras e utiliza indicadores financeiros de companhias abertas. Para as análises são utilizados dois conjuntos de ações, um do setor de petróleo e petroquímica e outro do setor têxtil, com indicadores financeiros relativos ao período de 1994 a 2005.

UM MODELO FUZZY-DEA-GAME PARA ESTRATÉGIAS DE PRODUÇÃO SOB INCERTEZA

Este trabalho desenvolve um novo modelo Fuzzy-DEA-Game (FDG) para apoiar o estabelecimento de estratégias de produção. Esse modelo combina a Análise Envoltória de Dados (DEA) com conceitos da Teoria dos Conjuntos Fuzzy e do Jogo da Barganha de Nash. O modelo permite uma avaliação da eficiência produtiva e econômica dos produtos, o que pode resultar num portfólio de produtos mais rentáveis e de interesse do mercado consumidor. O modelo foi aplicado em uma empresa do segmento de energia. Os resultados obtidos com a aplicação do modelo FDG mostraram-se aderentes à realidade da empresa estudada e forneceram metas para a redução dos níveis de recursos (entradas) necessários para a fabricação dos produtos e para aumento dos níveis de resultados (saídas) oriundos da comercialização desses produtos. Como resultado adicional importante, o modelo FDG permitiu a identificação dos produtos do portfólio que são mais sensíveis à ocorrência de incerteza.

Direct Power Control of Matrix Converter Based Unified Power Flow Controllers

This paper presents the Direct Power Control of Three-Phase Matrix Converters (DPC-MC) operating as Unified Power Flow Controllers (UPFC). Since matrix converters allow direct AC/AC power conversion without intermediate energy storage link, the resulting UPFC has reduced volume and cost, together with higher reliability. Theoretical principles of DPC-MC method are established based on an UPFC model, together with a new direct power control approach based on sliding mode control techniques. As a result, active and reactive power can be directly controlled by selection of an appropriate switching state of matrix converter. This new direct power control approach associated to matrix converters technology guarantees decoupled active and reactive power control, zero error tracking, fast response times and timely control actions. Simulation results show good performance of the proposed system.

A Pitch Control Malfunction Analysis for Wind Turbines with Permanent Magnet Synchronous Generator and Full-power Converters: Proportional Integral Versus Fractional-order Controllers

A transient analysis for two full-power converter wind turbines equipped with a permanent magnet synchronous generator is studied in this article, taking into consideration, as a new contribution to earlier studies, a pitch control malfunction. The two full-power converters considered are, respectively, a two-level and a multi-level converter. Moreover, a novel control strategy based on fractional-order controllers for wind turbines is studied. Simulation results are presented; conclusions are in favor of the novel control strategy, improving the quality of the energy injected into the electric grid.

Fuzzy linguistic model for evaluating the risk of neonatal death

OBJECTIVE: To introduce a fuzzy linguistic model for evaluating the risk of neonatal death. METHODS: The study is based on the fuzziness of the variables newborn birth weight and gestational age at delivery. The inference used was Mamdani's method. Neonatologists were interviewed to estimate the risk of neonatal death under certain conditions and to allow comparing their opinions and the model values. RESULTS: The results were compared with experts' opinions and the Fuzzy model was able to capture the expert knowledge with a strong correlation (r=0.96). CONCLUSIONS: The linguistic model was able to estimate the risk of neonatal death when compared to experts' performance.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.

Matrix Converter-Based Unified Power-Flow Controllers: Advanced Direct Power Control Method

This paper presents a direct power control (DPC) for three-phase matrix converters operating as unified power flow controllers (UPFCs). Matrix converters (MCs) allow the direct ac/ac power conversion without dc energy storage links; therefore, the MC-based UPFC (MC-UPFC) has reduced volume and cost, reduced capacitor power losses, together with higher reliability. Theoretical principles of direct power control (DPC) based on sliding mode control techniques are established for an MC-UPFC dynamic model including the input filter. As a result, line active and reactive power, together with ac supply reactive power, can be directly controlled by selecting an appropriate matrix converter switching state guaranteeing good steady-state and dynamic responses. Experimental results of DPC controllers for MC-UPFC show decoupled active and reactive power control, zero steady-state tracking error, and fast response times. Compared to an MC-UPFC using active and reactive power linear controllers based on a modified Venturini high-frequency PWM modulator, the experimental results of the advanced DPC-MC guarantee faster responses without overshoot and no steady-state error, presenting no cross-coupling in dynamic and steady-state responses.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Wind turbines equipped with fractional-order controllers: Stress on the mechanical drive train due to a converter control malfunction

This paper is on variable-speed wind turbines with permanent magnet synchronous generator (PMSG). Three different drive train mass models and three different topologies for the power-electronic converters are considered. The three different topologies considered are respectively a matrix, a two-level and a multilevel converter. A novel control strategy, based on fractional-order controllers, is proposed for the wind turbines. Simulation results are presented to illustrate the behaviour of the wind turbines during a converter control malfunction, considering the fractional-order controllers. Finally, conclusions are duly drawn. Copyright (C) 2010 John Wiley & Sons, Ltd.

Hybrid fuzzy Monte Carlo and logic programming model for distribution network reconfiguration in the presence of outages

This paper presents a methodology for distribution networks reconfiguration in outage presence in order to choose the reconfiguration that presents the lower power losses. The methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. Once obtained the system states by Monte Carlo simulation, a logical programming algorithm is applied to get all possible reconfigurations for every system state. In order to evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation a distribution power flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology to a practical case, the paper includes a case study that considers a real distribution network.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.