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.

Fault point location system

Abstract: A fault point location system comprising substations 1 to send information of transmission and distribution lines, and the master station 2 to locate fault points under the information. Substation 1 detects a surge detection time and the surge current polarity. Master 2 identifies a no-branch fault section under the polarity, and identifies the fault point under a difference among detection times of substations 1 at both ends of the section, and the section length.

A hybrid expert system for faulted section identification, fault type classification and selection of fault location algorithms

Abstract: This paper presents an expert system developed in turbo prolog to identify faulted sections and interpret protective apparatus operation in large interconnected power systems. The expert system presented here is capable of identifying bus faults, line fault sections, and fault sections in the common area of a specific bus and line. Also, the expert system identifies relays or breakers malfunctions. The expert system is then expanded to include real-time measurements of current and voltage phasors to classify the type of fault that the faulted section has experienced. Furthermore, when the faulted section is a transmission line, the expert system selects an appropriate fault location algorithm to compute the fault location in miles. This paper shows that the combination of numeric and data base algorithm is essential to many developments in expert system application in power systems. Evaluating the expert systems reported so far for fault diagnosis reveals that all of these schemes utilize only the data received from breaker and relay status. Consider the recent trend in digital protection, real-time phasor measurements would be available. To combine real-time phasor measurements with relay and breaker status, a hybrid expert system is required. A hybrid expert system combines numeric algorithms with data base algorithms in one scheme. This paper recognizes this feature in the expert system developed here. The expert system reported in this paper includes four stages. The first stage determines the faulted section of the power system and reports correct and incorrect breaker and relay operation.

Fault Location in Ungrounded Photovoltaic System Using Wavelets and ANN

Abstract: Identifying ground faults is a significant problem in ungrounded photovoltaic (PV) systems because such earth faults do not provide sufficient fault currents for their detection and location during system operation. If such ground faults are not cleared quickly, a subsequent ground fault on the healthy phase will create a complete short circuit in the system. This paper proposes a novel fault-location scheme in which high frequency noise patterns are used to identify the fault location. The high-frequency noise is generated due to the switching transients of converters combined with the parasitic capacitance of PV panels and cables. Discrete wavelet transform is used for the decomposition of the monitored signal (midpoint voltage of the converters) and features are extracted. Norm values of the measured waveform at different frequency bands give unique features at different fault locations and are used as the feature vectors for pattern recognition. Then, a three-layer feedforward artificial neural networks classifier, which can automatically classify the fault locations according to the extracted features, is investigated. The proposed fault-location scheme has been primarily developed for fault location in the PV farm (PV panels and dc cables). The method is tested for ground faults as well as line–line faults. These faults are simulated with a real-time digital simulator and the data are then analyzed with wavelets. Finally, the effectiveness of the designed fault locator is tested with varying system parameters. The results demonstrate that the proposed approach has accurate and robust performance even with noisy measurements and changes in operating conditions.

Fault Location for Underground Power Cable Using Distributed Parameter Approach

Abstract: This paper proposes an extensive fault location model for underground power cable in distribution system using voltage and current measurements at the sending-end. First, an equivalent circuit that models a faulted underground cable system is analyzed using distributed parameter approach. Then, the analysis of sequence networks in three-phase network is obtained by applying the boundary conditions. This analysis is used to calculate a fault distance in single section using voltage and current equations. The extension to multi-section is further analyzed based on Korean distribution systems. This method is an iterative process to determine a faulted section from the network. Finally, the case studies are evaluated with variations of fault distance and resistance, which also includes the evaluation of its robustness to load uncertainty.

Fault-signature modeling and detection of inner-race bearing faults

Abstract: This paper develops a fault-signature model and a fault-detection scheme for using machine vibration to detect inner-race defects. To motivate this research, it is explained and illustrated with experimental results why fault signatures from nonouter-race defects (e.g., inner-race defects) can be less salient than those from outer-race defects. Then, a signal model is presented for the production and propagation of an inner-race fault signature; this model is then used to design an inner-race fault-detection scheme. This scheme examines machine-vibration spectra for peaks with phase-coupled sidebands occurring at a spacing predicted by the model. The proficiency of this fault-detection scheme at detecting inner-race bearing faults is then experimentally verified with results from 12 bearings representing varying degrees of fault severity.