An approach based on neural networks for identification of fault sections in radial distribution systems

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

Test results of fault current limiter using YBCO tapes with shunt protection

A Fault Current Limiter (FCL) based on high temperature superconducting elements with four tapes in parallel were designed and tested in 220 V line for a fault current peak between 1 kA to 4 kA. The elements employed second generation (2G) HTS tapes of YBCO coated conductor with stainless steel reinforcement. The tapes were electrically connected in parallel with effective length of 0.4 m per element (16 elements connected in series) constituting a single-phase unit. The FCL performance was evaluated through over-current tests and its recovery characteristics under load current were analyzed using optimized value of the shunt protection. The projected limiting ratio achieved a factor higher than 4 during fault of 5 cycles without degradation. Construction details and further test results will be shown in the paper. © 2010 IOP Publishing Ltd.

Thermotectonic and fault dynamic analysis of Precambrian basement and tectonic constraints with the Parana basin

The Precambrian crystalline basement of southeast Brazil is affected by many Phanerozoic reactivations of shear zones that developed during the end of the Neoproterozoic in the Brasiliano orogeny. These reactivations with specific tectonic events, a multidisciplinary study was done, involving geology, paleostress, and structural analysis of faults, associated with apatite fission track methods along the northeastern border of the Parana basin in southeast Brazil.The results show that the study area consists of three main tectonic domains, which record different episodes of uplift and reactivation of faults. These faults were brittle in character and resulted in multiple generations of fault products as pseudotachylytes and ultracataclasites, foliated cataclasites and fault gouges.Based on geological evidence and fission track data, an uplift of basement rocks and related tectonic subsidence with consequent deposition in the Parana basin were modeled.The reactivations of the basement record successive uplift events during the Phanerozoic dated via corrected fission track ages, at 387 +/- 50 Ma (Ordovician); 193 +/- 19 Ma (Triassic); 142 +/- 18 Ma (Jurassic), 126 +/- 11 Ma (Early Cretaceous); 89 +/- 10 Ma (Late Cretaceous) and 69 +/- 10 Ma (Late Cretaceous). These results indicate differential uplift of tectonic domains of basement units, probably related to Parana basin subsidence. Six major sedimentary units (supersequences) that have been deposited with their bounding unconformities, seem to have a close relationship with the orogenic events during the evolution of southwestern Gondwana. (c) 2005 Elsevier Ltd. All rights reserved.

Fault Current Limiter Using YBCO Coated Conductor-The Limiting Factor and Its Recovery Time

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Well-conditioned robust observer for detection of family systems components

A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

Well-conditioned robust observer for detection of faulty systems components

A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Development of an instrumentation system embedded on FPGA for real time measurement of mechanical vibrations in rotating machinery

The real-time monitoring of events in an industrial plant is vital, to monitor the actual conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes condition monitoring of the rotating machinery, to anticipate possible conditions of failure. To increase the operational reliability it is thus necessary an efficient tool to analyze and monitor the equipments, in real-time, and enabling the detection of e.g. incipient faults in bearings. To fulfill these requirements some innovations have become frequent, namely the inclusion of vibration sensors or stator current sensors. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades, and replacement of the monitored machine, as well as expansion of the monitoring system. This paper presents the development, implementation and testing of an instrument for vibration monitoring, as a possible solution to embed in industrial environment. The digital control system is based on an FPGA, and its configuration with an open hardware design tool is described. Special focus is given to the area of fault detection in rolling bearings. © 2012 IEEE.

Analise de desempenho de observadores de estado robustos na localizacao de falhas

The state observers can reconstruct and monitor unmeasurable states. A new concept of fault detection and isolation using state observers is presented. The method selects the parameters from components that may fail during the process and constructs optimized robust observers. To isolate component failures via robust observation, a bank of detection observers is organized, in which each observer is only sensitive to one specified component failure while robust to all other component failures. This paper analyzes the performance of transient and steady-state behavior of the state observer.

Detecção e classificação de faltas de curto-circuito em sistemas de distribuição de energia elétrica usando lógica nebulosa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Utilização de modelos de falhas e observadores de estado em estruturas reticuladas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Diagnose de falhas via observadores de estado em sistemas mecânicos com absorvedores dinâmicos de vibrações tipo lâmina vibrante

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Caracterização estrutural de pós de 'SRTIO IND.3' puro e dopado com samário

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento de um sistema de instrumentação de medição de vibração mecânica em máquina rotativas, em tempo real, embarcado em FPGA

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)