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.

A calibration approach based on Takagi-Sugeno fuzzy inference system for digital electronic compasses

Recently, the development of industrial processes brought on the outbreak of technologically complex systems. This development generated the necessity of research relative to the mathematical techniques that have the capacity to deal with project complexities and validation. Fuzzy models have been receiving particular attention in the area of nonlinear systems identification and analysis due to it is capacity to approximate nonlinear behavior and deal with uncertainty. A fuzzy rule-based model suitable for the approximation of many systems and functions is the Takagi-Sugeno (TS) fuzzy model. IS fuzzy models are nonlinear systems described by a set of if then rules which gives local linear representations of an underlying system. Such models can approximate a wide class of nonlinear systems. In this paper a performance analysis of a system based on IS fuzzy inference system for the calibration of electronic compass devices is considered. The contribution of the evaluated IS fuzzy inference system is to reduce the error obtained in data acquisition from a digital electronic compass. For the reliable operation of the TS fuzzy inference system, adequate error measurements must be taken. The error noise must be filtered before the application of the IS fuzzy inference system. The proposed method demonstrated an effectiveness of 57% at reducing the total error based on considered tests. (C) 2011 Elsevier Ltd. All rights reserved.

Fuzzy Control System for Voltage Regulation In Power Transformers

A fuzzy control strategy for voltage regulation in electric power distribution systems is introduced in this article. This real-time controller would act on power transformers equipped with under-load tap changers. The fuzzy system was employed to turn the voltage-control relays into adaptive devices. The scope of the present study has been limited to the power distribution substation, and both 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 both the requirements of the Brazilian regulatory bodies and the real concerns of the electric power distribution companies. A prototype based on the fuzzy control strategy proposed in this paper has also been implemented for validation purposes and its experimental results were highly satisfactory.

An expert flow system involving in-line prior assay for turbidimetric determination of chloride and sulphate in natural waters

A multi-pumping flow system exploiting prior assay is proposed for sequential turbidimetric determination of sulphate and chloride in natural waters. Both methods are implemented in the same manifold that provides facilities for: in-line sample clean-up with a Bio-Rex 70 mini-column with fluidized beads: addition of low amounts of sulphate or chloride ions to the reaction medium for improving supersaturation; analyte precipitation with Ba(2+) or Ag(+); real-time decision on the need for next assay. The sample is initially run for chloride determination, and the analytical signal is compared with a preset value. If higher, the sample is run again, now for sulphate determination. The strategy may lead to all increased sample throughput. The proposed system is computer-controlled and presents enhanced figures of merit. About 10 samples are run per hour (about 60 measurements) and results are reproducible and Unaffected by the presence of potential interfering ions at concentration levels usually found in natural waters. Accuracy was assessed against ion chromatography. (C) 2008 Elsevier B.V. All rights reserved.

Embedded DSP-Based Compact Fuzzy System and Its Application for Induction-Motor V/f Speed Control

This paper presents a compact embedded fuzzy system for three-phase induction-motor scalar speed control. The control strategy consists in keeping constant the voltage-frequency ratio of the induction-motor supply source. A fuzzy-control system is built on a digital signal processor, which uses speed error and speed-error variation to change both the fundamental voltage amplitude and frequency of a sinusoidal pulsewidth modulation inverter. An alternative optimized method for embedded fuzzy-system design is also proposed. The controller performance, in relation to reference and load-torque variations, is evaluated by experimental results. A comparative analysis with conventional proportional-integral controller is also achieved.

Model for Differential Nursing Diagnosis of Alterations in Urinary Elimination Based on Fuzzy Logic

Nursing diagnoses associated with alterations of urinary elimination require different interventions, Nurses, who are not specialists, require support to diagnose and manage patients with disturbances of urine elimination. The aim of this study was to present a model based on fuzzy logic for differential diagnosis of alterations in urinary elimination, considering nursing diagnosis approved by the North American Nursing Diagnosis Association, 2001-2002. Fuzzy relations and the maximum-minimum composition approach were used to develop the system. The model performance was evaluated with 195 cases from the database of a previous study, resulting in 79.0% of total concordance and 19.5% of partial concordance, when compared with the panel of experts. Total discordance was observed in only three cases (1.5%). The agreement between model and experts was excellent (kappa = 0.98, P < .0001) or substantial (kappa = 0.69, P < .0001) when considering the overestimative accordance (accordance was considered when at least one diagnosis was equal) and the underestimative discordance (discordance was considered when at least one diagnosis was different), respectively. The model herein presented showed good performance and a simple theoretical structure, therefore demanding few computational resources.

Fuzzy risk index for power transformer failures due to external short-circuits

A novel methodology to assess the risk of power transformer failures caused by external faults, such as short-circuit, taking the paper insulation condition into account, is presented. The risk index is obtained by contrasting the insulation paper condition with the probability that the transformer withstands the short-circuit current flowing along the winding during an external fault. In order to assess the risk, this probability and the value of the degree of polymerization of the insulating paper are regarded as inputs of a type-2 fuzzy logic system (T2-FLS), which computes the fuzzy risk level. A Monte Carlo simulation has been used to find the survival function of the currents flowing through the transformer winding during a single-phase or a three-phase short-circuit. The Roy Billinton Test System and a real power system have been used to test the results. (C) 2008 Elsevier B.V. All rights reserved.

Computer-aided Structure Elucidation of Neolignans

This paper describes a new module of the expert system SISTEMAT used for the prediction of the skeletons of neolignans by (13)C NMR, (1)H NMR and botanical data obtained from the literature. SISTEMAT is composed of MACRONO, SISCONST, C13MACH, H1MACH and SISOCBOT programs, each analyzing data of the neolignan in question to predict the carbon skeleton of the compound. From these results, the global probability is computed and the most probable skeleton predicted. SISTEMAT predicted the skeletons of 75% of the 20 neolignans tested, in a rapid and simple procedure demonstrating its advantage for the structural elucidation of new compounds.

Computer-Assisted Approach to Structural Elucidation of Lignans

This paper reports an expert system (SISTEMAT) developed for structural determination of diverse chemical classes of natural products, including lignans, based mainly on 13C NMR and 1H NMR data of these compounds. The system is composed of five programs that analyze specific data of a lignan and shows a skeleton probability for the compound. At the end of analyses, the results are grouped, the global probability is computed, and the most probable skeleton is exhibited to the user. SISTEMAT was able to properly predict the skeletons of 80% of the 30 lignans tested, demonstrating its advantage during the structural elucidation course in a short period of time.

Combined heuristic with fuzzy system to transmission system expansion planning

A heuristic algorithm that employs fuzzy logic is proposed to the power system transmission expansion planning problem. The algorithm is based on the divide to conquer strategy, which is controlled by the fuzzy system. The algorithm provides high quality solutions with the use of fuzzy decision making, which is based on nondeterministic criteria to guide the search. The fuzzy system provides a self-adjusting mechanism that eliminates the manual adjustment of parameters to each system being solved. (C) 2010 Elsevier B.V. All rights reserved.

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.

Power Transformer Differential Protection Based on Clarke`s Transform and Fuzzy Systems

The power transformer is a piece of electrical equipment that needs continuous monitoring and fast protection since it is very expensive and an essential element for a power system to perform effectively. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can affect the protection behavior and the power system stability. This paper proposes the development of a new algorithm to improve the differential protection performance by using fuzzy logic and Clarke`s transform. An electrical power system was modeled using Alternative Transients Program (ATP) software to obtain the operational conditions and fault situations needed to test the algorithm developed. The results were compared to a commercial relay for validation, showing the advantages of the new method.

Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning

Recent advances in energy technology generation and new directions in electricity regulation have made distributed generation (DG) more widespread, with consequent significant impacts on the operational characteristics of distribution networks. For this reason, new methods for identifying such impacts are needed, together with research and development of new tools and resources to maintain and facilitate continued expansion towards DG. This paper presents a study aimed at determining appropriate DG sites for distribution systems. The main considerations which determine DG sites are also presented, together with an account of the advantages gained from correct DG placement. The paper intends to define some quantitative and qualitative parameters evaluated by Digsilent (R), GARP3 (R) and DSA-GD software. A multi-objective approach based on the Bellman-Zadeh algorithm and fuzzy logic is used to determine appropriate DG sites. The study also aims to find acceptable DG locations both for distribution system feeders, as well as for nodes inside a given feeder. (C) 2010 Elsevier Ltd. All rights reserved.

Fuzzy pole placement based on piecewise Lyapunov functions

This paper presents a controller design method for fuzzy dynamic systems based on piecewise Lyapunov functions with constraints on the closed-loop pole location. The main idea is to use switched controllers to locate the poles of the system to obtain a satisfactory transient response. It is shown that the global fuzzy system satisfies the requirements for the design and that the control law can be obtained by solving a set of linear matrix inequalities, which can be efficiently solved with commercially available softwares. An example is given to illustrate the application of the proposed method. Copyright (C) 2009 John Wiley & Sons, Ltd.

A classification methodology for the risk of weed infestation using fuzzy logic

Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.