Is Fault Location Killing Our Cable Systems?

Lanz, B.; Sanchez, Eugene

doi:10.1109/tdc.2016.7520056

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…They will conduct visual inspections together with measurement to estimate the cause of the disturbance. After identifying the cause of the disturbance, if it is due to fault occurrence, methods such as capacitive discharge and pulse echo meter will be used to obtain the estimated location of fault [5].…”

Section: Introductionmentioning

confidence: 99%

Fault classification in smart distribution network using support vector machine

Chuan

Aziz

Yasin

et al. 2020

IJEECS

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<span>Machine learning application have been widely used in various sector as part of reducing work load and creating an automated decision making tool. This has gain the interest of power industries and utilities to apply machine learning as part of the operation. Fault identification and classification based machine learning application in power industries have gain significant accreditation due to its great capability and performance. In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. Eleven different types of faults are generated with respect to actual network. A wide range of simulation condition in terms of different fault impedance value as well as fault types are considered in training and testing data. Right setting parameters are important to learning results and generalization ability of SVM. Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. Initial finding from simulation result indicates that the proposed method is quick in learning and shows good accuracy values on faults type classification in distribution system. The developed algorithm is tested on IEEE 34 bus and IEEE 123 bus test distribution system. </span>

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Section: Introductionmentioning

confidence: 99%