2015
DOI: 10.1109/tpwrs.2014.2375816
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A Novel Method for Single and Simultaneous Fault Location in Distribution Networks

Abstract: This paper introduces a novel method for single and simultaneous fault location in distribution networks by means of a sparse representation (SR) vector, Fuzzy-clustering, and machinelearning. The method requires few smart meters along the primary feeders to measure the pre-and during-fault voltages. The voltage sag values for the measured buses produce a vector whose dimension is less than the number of buses in the system. By concatenating the corresponding rows of the bus impedance matrix, an underdetermine… Show more

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Cited by 98 publications
(46 citation statements)
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“…Equations 11-13 express first estimations of load demand for all load types except constant power loads. First, the total power injected to grid (S inj ) is computed using Equation 11. Then, the demand of all constant power loads will be subtracted from S inj , because the power consumption for this type of load is known.…”
Section: First Estimation Of Loads Demandmentioning
confidence: 99%
“…Equations 11-13 express first estimations of load demand for all load types except constant power loads. First, the total power injected to grid (S inj ) is computed using Equation 11. Then, the demand of all constant power loads will be subtracted from S inj , because the power consumption for this type of load is known.…”
Section: First Estimation Of Loads Demandmentioning
confidence: 99%
“…Unlike the conventional impedance based methods, the relationships between the changes of the system impedance (caused by different faults) and the changes of the voltages at different system nodes are used. The voltage variations (voltage sag/increase) are monitored [19][20][21][22][23][24][25][26] to locate faults. The system pre-fault and post-fault steady state voltages at different system nodes are recorded to calculate the voltage sags of different fault scenarios and a map of the voltage variations for different faults can be derived by off-line simulations.…”
Section: Introductionmentioning
confidence: 99%
“…The authors in [6] discuss a method for identifying faulted segments on multi-phase distribution primaries using sequence component modeling and standard three-phase short-circuit solvers. Machine-learning is utilized in [7] to pinpoint fault location by using smart meters along the primary feeders. The author of [8] presents a fault location method for radial distribution networks.…”
Section: Introductionmentioning
confidence: 99%