scispace - formally typeset
Search or ask a question
Topic

Fault indicator

About: Fault indicator is a research topic. Over the lifetime, 10057 publications have been published within this topic receiving 143482 citations. The topic is also known as: FCI & power line fault indicator.


Papers
More filters
Patent
26 Sep 2005
TL;DR: In this article, a fault module supports detection, analysis, and/or logging of various faults in a processor system, and it is provided on a multi-core, single die device.
Abstract: A fault module supports detection, analysis, and/or logging of various faults in a processor system. In one embodiment, the system is provided on a multi-core, single die device.

122 citations

Journal ArticleDOI
Yuan Liao1
TL;DR: In this paper, a fault location approach for single-circuit lines utilizing only voltage measurements from one or two buses, which may be distant from the faulted line, is presented.
Abstract: Diverse transmission line fault location algorithms have been proposed in the past depending on measurements available. Existing algorithms usually require measurements captured from buses of a faulted line. By taking advantage of the bus-impedance matrix technique, this paper presents a possible fault location approach for single-circuit lines utilizing only voltage measurements from one or two buses, which may be distant from the faulted line. With the addition of a fictitious bus where the fault occurs, the transfer impedances of this bus and other buses are revealed as a function of the fault location. Based on the relationship between the bus voltage change due to fault and the transfer impedance, the fault location can be derived. Shunt capacitance of the line is ignored first and then fully considered based on distributed parameter line model. Electromagnetic transients program simulation studies have shown quite encouraging results.

122 citations

Journal ArticleDOI
TL;DR: In this paper, the authors employed convolutional neural network (CNN) as a well-established and powerful deep learning algorithm for tool wear estimation and proposed a hybrid feature extraction method using wavelet time-frequency transformation and spectral subtraction algorithms.
Abstract: Process monitoring is necessary in machining operation to increase productivity, improve surface quality, and reduce unscheduled downtime. Tool wear and breakage are important and common source of machining problems due to high temperatures and forces of the machining process. Therefore, it is highly beneficial to develop an online tool condition monitoring (TCM) system. This paper investigates a robust tool wear monitoring system for milling operation. Recent developments in machine learning, in particular deep learning methods, result in significant improvement in automation of different industries. Therefore, in this research, we employed convolutional neural network (CNN) as a well-established and powerful deep learning algorithm for tool wear estimation. Wavelet packet-based features are extracted for tool wear monitoring as a powerful time-frequency fault indicator. Moreover, a hybrid feature extraction method is proposed using wavelet time-frequency transformation and spectral subtraction algorithms to intensify the effect of tool wear in the signal and reduce the effect of other cutting parameters. CNN-based monitoring systems are compared with three other machine learning methods (support vector machine, Bayesian rigid network, and K nearest neighbor method) as the baseline. The research is validated using different datasets. The algorithms are implemented and compared using experimental force and vibration signals from LIPPS lab of ETS university as well as using current signals as the fault indicator from Nasa_Ames dataset.

122 citations

Journal ArticleDOI
TL;DR: In this paper, a frequency domain approach for digital relaying of transmission line faults mitigating the adverse effects of power swing on conventional distance relaying is presented, which is different from conventional algorithms that are based on deterministic computations on a well defined model for transmission line protection.
Abstract: In the present milieu, changes in regulations and the opening of power markets have manifested in the form of large amount of power transfer across transmission lines with frequent changes in loading conditions based on market price. Since conventional distance relays may consider power swing as a fault, tripping because of such malfunctioning would lead to serious consequences for power system stability. A frequency domain approach for digital relaying of transmission line faults mitigating the adverse effects of power swing on conventional distance relaying is presented. A wavelet-neuro-fuzzy combined approach for fault location is also presented. It is different from conventional algorithms that are based on deterministic computations on a well-defined model for transmission line protection. The wavelet transform captures the dynamic characteristics of fault signals using wavelet multi-resolution analysis (MRA) coefficients. The fuzzy inference system (FIS) and the adaptive-neuro-fuzzy inference system (ANFIS) are both used to extract important features from wavelet MRA coefficients and thereby to reach conclusions regarding fault location. Computer simulations using MATLAB have been conducted for a 300 km, 400 kV line and results indicate that the proposed localisation algorithm is immune to effects of fault inception, angle and distance. The results contained here validate the superiority of the ANFIS approach over the FIS for fault location.

121 citations

Journal ArticleDOI
TL;DR: It is found that it is possible to suitably detect arc faults by means of a high-resolution low-frequency harmonic analysis of current signal, based on chirp zeta transform, and a proper set of indicators.
Abstract: This paper presents a method for the detection of series arc faults in electrical circuits, which has been developed starting from an experimental characterization of the arc fault phenomenon and an arcing current study in several test conditions. Starting from this, the authors have found that is it possible to suitably detect arc faults by means of a high-resolution low-frequency harmonic analysis of current signal, based on chirp zeta transform, and a proper set of indicators. The proposed method effectiveness is shown by means of experimental tests, which were carried in both arcing and nonarcing conditions and in the presence of different loads, chosen according to the UL 1699 standard requirements.

121 citations


Network Information
Related Topics (5)
Electric power system
133K papers, 1.7M citations
91% related
Voltage
296.3K papers, 1.7M citations
85% related
Control theory
299.6K papers, 3.1M citations
84% related
Control system
129K papers, 1.5M citations
84% related
Wind power
99K papers, 1.5M citations
82% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202381
2022215
202127
202061
2019116
2018160