Sistema inteligente baseado em decomposição por componentes ortogonais e inferência fuzzy para localização de faltas de alta impedância em sistemas de distribuição de energia elétrica com geração distribuída

Os sistemas elétricos de potência modernos apresentam inúmeros desafios em sua operação. Nos sistemas de distribuição de energia elétrica, devido à grande ramificação, presença de extensos ramais monofásicos, à dinâmica das cargas e demais particularidades inerentes, a localização de faltas representa um dos maiores desafios. Das barreiras encontradas, a influência da impedância de falta é uma das maiores, afetando significativamente a aplicação dos métodos tradicionais na localização, visto que a magnitude das correntes de falta é similar à da corrente de carga. Neste sentido, esta tese objetivou desenvolver um sistema inteligente para localização de faltas de alta impedância, o qual foi embasado na aplicação da técnica de decomposição por componentes ortogonais no pré-processamento das variáveis e inferência fuzzy para interpretar as não-linearidades do Sistemas de Distribuição com presença de Geração Distribuída. Os dados para treinamento do sistema inteligente foram obtidos a partir de simulações computacionais de um alimentador real, considerando uma modelagem não-linear da falta de alta impedância. O sistema fuzzy resultante foi capaz de estimar as distâncias de falta com um erro absoluto médio inferior a 500 m e um erro absoluto máximo da ordem de 1,5 km, em um alimentador com cerca de 18 km de extensão. Tais resultados equivalem a um grau de exatidão, para a maior parte das ocorrências, dentro do intervalo de ±10%.

SAS/OWA:ordered weighted averaging in SAS optimization

This paper explores the use of the optimization procedures in SAS/OR software with application to the ordered weight averaging (OWA) operators of decision-making units (DMUs). OWA was originally introduced by Yager (IEEE Trans Syst Man Cybern 18(1):183-190, 1988) has gained much interest among researchers, hence many applications such as in the areas of decision making, expert systems, data mining, approximate reasoning, fuzzy system and control have been proposed. On the other hand, the SAS is powerful software and it is capable of running various optimization tools such as linear and non-linear programming with all type of constraints. To facilitate the use of OWA operator by SAS users, a code was implemented. The SAS macro developed in this paper selects the criteria and alternatives from a SAS dataset and calculates a set of OWA weights. An example is given to illustrate the features of SAS/OWA software. © Springer-Verlag 2009.

Real-Time Voltage Regulation in Power Distribution System Using Fuzzy Control

Two different fuzzy approaches to voltage control in electric power distribution systems are introduced in this paper. The real-time controller in each case would act on power transformers equipped with under-load tap changers. Learning systems are employed to turn the voltage-control relays into adaptive devices. The scope of this study has been limited to the power distribution substation, and the voltage measurements and control actions are carried out on the secondary bus. The capacity of fuzzy systems to handle approximate data, together with their unique ability to interpret qualitative information, make it possible to design voltage-control strategies that satisfy the requirements of the Brazilian regulatory bodies and the real concerns of the electric power distribution companies. Fuzzy control systems based on these two strategies have been implemented and the test results were highly satisfactory.

Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems

An efficient expert system for the power transformer condition assessment is presented in this paper. Through the application of Duval`s triangle and the method of the gas ratios a first assessment of the transformer condition is obtained in the form of a dissolved gas analysis (DGA) diagnosis according IEC 60599. As a second step, a knowledge mining procedure is performed, by conducting surveys whose results are fed into a first Type-2 Fuzzy Logic System (T2-FLS), in order to initially evaluate the condition of the equipment taking only the results of dissolved gas analysis into account. The output of this first T2-FLS is used as the input of a second T2-FLS, which additionally weighs up the condition of the paper-oil system. The output of this last T2-FLS is given in terms of words easily understandable by the maintenance personnel. The proposed assessing methodology has been validated for several cases of transformers in service. (C) 2010 Elsevier Ltd. All rights reserved.

Data-driven system identification via evolutionary retrieval of Takagi-Sugeno fuzzy models

Systemidentification, evolutionary automatic, data-driven model, fuzzy Takagi-Sugeno grammar, genotype interpretability, toxicity-prediction

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Application of a Pid+fuzzy controller on the motion control system in machine tools

This work deals with an hybrid PID+fuzzy logic controller applied to control the machine tool biaxial table motions. The non-linear model includes backlash and the axis elasticity. Two PID controllers do the primary table control. A third PID+fuzzy controller has a cross coupled structure whose function is to minimise the trajectory contour errors. Once with the three PID controllers tuned, the system is simulated with and without the third controller. The responses results are plotted and compared to analyse the effectiveness of this hybrid controller over the system. They show that the proposed methodology reduces the contour error in a proportion of 70:1.

Fuzzy expert system in the prediction of neonatal resuscitation

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5% and specificity of 94.8% in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

A fuzzy rule-based decision support system for Duodopa treatment in Parkinson

A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.

A fuzzy inference system for identification of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behavior. In this paper, a system based on fuzzy logic systems is developed to overcome the problems usually found in the conventional mathematical models. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the fuzzy approach. Simulation results are presented to justify the validity of the proposed approach.

A controller design used in Lorenz synchronization chaotic system for secure communication based on LMI fuzzy control system

This paper describes a mathematical study about chaotic system and about the unified approach of chaos control via fuzzy control system based in Linear Matrix Inequality to design a controller which synchronizes the transmission/reception system. This system, that was based in Lorenz chaotic circuit, can be used for transmit signals in secure way.

Case-based system: Indexing and retrieval with fuzzy hypercube

In some applications with case-based system, the attributes available for indexing are better described as linguistic variables instead of receiving numerical treatment. In these applications, the concept of fuzzy hypercube can be applied to give a geometrical interpretation of similarities among cases. This paper presents an approach that uses geometrical properties of fuzzy hypercube space to make indexing and retrieval processes of cases.

Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. © Springer-Verlag Berlin Heidelberg 2007.

A proposal for reliability evaluation of components on electric power distribution system integrating probabilistic models and fuzzy inference systems

The system reliability depends on the reliability of its components itself. Therefore, it is necessary a methodology capable of inferring the state of functionality of these components to establish reliable indices of quality. Allocation models for maintenance and protective devices, among others, have been used in order to improve the quality and availability of services on electric power distribution systems. This paper proposes a methodology for assessing the reliability of distribution system components in an integrated way, using probabilistic models and fuzzy inference systems to infer about the operation probability of each component. © 2012 IEEE.