A Comprehensive Review of Fault Location Methods for Distribution Power System

Gana, N; Aziz, Nur Fadilah Ab; Ali, Ziad M.; Hashim, Habibah; Yunus, Bahisham

doi:10.11591/ijeecs.v6.i1.pp185-192

Cited by 10 publications

(7 citation statements)

References 38 publications

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“…Unlike low impedance, high impedance faults are difficult to be detected (Wester, 1998). It requires customized approaches that can extract information of these high impedance faults from the available data occurrence (Gana et al, 2017). The shunt faults have been researched to be more severe than the series faults.…”

Section: Faults In Distribution Systemsmentioning

confidence: 99%

“…One of the major menaces of faults on the distribution networks is the negative impact on the system reliability while also increasing the operational and maintenance costs (Gana et al, 2017). To this end, the utility companies have devised different means to contain the excesses of faults on the network reliability by adopting enhanced preventive maintenance policies and deployment of standard protection mechanisms in their systems.…”

Section: Introductionmentioning

confidence: 99%

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Vulnerability Assessment of Components in a Typical Rural Nigerian Power Distribution System

Omoroghomwan

Igbinovia

Odiase

2021

Nig. J. Env. Sci. & Tech.

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The major aim of any power system is the continuous provision of safe, quality and reliable electric power to the customers. One of the greatest challenges to meeting up with this goal is the failure of components in the system. In this article, the frequency of outages caused by failure of different components in the distribution system was investigated to ascertain the ones that are more susceptible to failure by comparing their proportions in the entire failure events. The outage data obtained from Irrua Transmission Station comprising Ehor, Ubiaja and Uzebba 33kV feeders were analyzed using Microsoft Excel while the hazard rates were measured using the failure rate index. Findings revealed that 93.77% of all the forced outages in the distribution subsystem in the power sector are caused by the high exposure rate of the bare aluminum conductors used in the construction of the various overhead feeders. Subsequently, the yearly failure rates of aluminum conductors, cross arms, relay, insulators, fuses, electric poles, breakers, transformers, isolators, cables lightning surge arresters were found to be 836.0, 17.5, 17.0, 10.3, 4.3, 2.0, 1.5, 1.3, 1.0, 0.5 and 0.3 respectively in the studied network. A comparison between this study and a related work showed that the rural feeders are more prone to faults as compared to the ones in the urban areas. It was therefore recommended that regular tree trimming along the network corridor should be done. Proper conductor size should be used in every subsequent construction and every segment with undersized conductor should be replaced with the appropriate size. This study will help the power system engineers in the design, construction, maintenance and operation of the distribution power system for optimum and improved system performance.

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Section: Faults In Distribution Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vulnerability Assessment of Components in a Typical Rural Nigerian Power Distribution System

Omoroghomwan

Igbinovia

Odiase

2021

Nig. J. Env. Sci. & Tech.

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“…Recently, machine learning has been speedily developed and applied successfully to classify types of fault on either transmission or distribution system [10]. However, majority of the research focus on the fault identification for overhead power transmission system [9][10][11][12][13][14][15] but not for the power distribution system. A quick method to predict the fault occurrence is needed since an instant operation is required to avoid the power distribution system from an outage.…”

Section: Svm As the Application Of Machine Learningmentioning

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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“…Faults in transmission lines affect the continuity of electrical power supply, damage electrical devices, endanger the human personnel that may be exposed to live parts and sometimes can even cause natural disasters such as fires. Therefore, a rapid and precise fault location would be of great interest to power utility companies that would be able to restore quickly and accurately the well-functioning of transmission lines by fixing the problems identified at the fault points [1]. Among the most used methods in fault localization, there is the one based on the fault impedance and it uses essentially the RMS values of voltages and currents at the fundamental frequency [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Traveling wave based fault location for power transmission lines using morphological filters and clarke modal components

Khoudry¹,

Belfqih²,

Boukherouaa³

et al. 2020

IJECE

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This article presents a fast and accurate fault location approach for power transmission lines based on the theory of traveling waves. In fact, when faults occur, they give rise to transient voltages and currents that propagate at a speed close to that of light along the transmission line as traveling waves. Moreover, according to the superposition theorem, each of these transients is a combination of a steady-state quantity and an incremental quantity. These transient signals measured at both ends of the line are first transformed to the Clarke (0-α-β components) components in order to categorize the type of faults, and then multi-scale morphological gradient filters are used to extract equivalent quantities to the incremental quantities to form what are called characteristic signals. These latter will be used to identify the fault location according to the proposed algorithm.

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A Comprehensive Review of Fault Location Methods for Distribution Power System

Abstract: Abstract

Cited by 10 publications

References 38 publications

Vulnerability Assessment of Components in a Typical Rural Nigerian Power Distribution System

Vulnerability Assessment of Components in a Typical Rural Nigerian Power Distribution System

Fault classification in smart distribution network using support vector machine

Traveling wave based fault location for power transmission lines using morphological filters and clarke modal components

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