Transmission line fault location for phase-to-earth fault using one-terminal data

Zhang, Q.; Zhang, Y.; Song, Wen-Zhan; Yu, Yixin

doi:10.1049/ip-gtd:19990174

Cited by 34 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithms to detect the faults have been developed vastly during the last decades. Algorithms like; the differential equation method, the steady-state method, and travelling wave approach (Lian 1994) for one-end algorithm (Zhang 1999) or for two-ended algorithm (Sheng 1998). Moreover, two sampling techniques were developed for the latter approach, which are; ZANCO Journal of Pure and Applied Sciences 2019 synchronized (Kezunovic1994) and nonsynchronized (Novosel996) methods.…”

Section: Introductionmentioning

confidence: 99%

Fault Bus Identification in Kurdistan Power Systems using Artificial Neural Network

2019

ZJPAS

View full text Add to dashboard Cite

A B S T R A C T:High voltage transmission lines are utilized to transmit electrical energy from the source to the substations. If any fault and disturbance are generated in the transmission lines and not detected, located and eliminated quickly, it may cause instability in the system. This paper presents fault location recognition in Kurdistan Regional power transmission system using artificial neural network (ANN). Load flow and short circuit calculations were performed with Power World Simulator (PWS) software. All Kurdistan region power system has been divided into 40 buses. The calculated results of the currents and the voltages at both line ends were used to train the ANN in Matlab to obtain correct fault location. The training testing and evaluation of the intelligent locator is done based on a multilayer perceptron feed forward artificial neural network with back propagation algorithm. The ANN used to locate the fault have been trained with different available sets of data from the selected power system model. Several algorithms have been carried out in order to train the network such that it locates the fault based on the input data provided. Proposed algorithm was developed by injecting the data randomly and massively to in rich the trained network. None-trained data has been used to validate the network and the network was able to locate the faults exactly.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault Bus Identification in Kurdistan Power Systems using Artificial Neural Network

2019

ZJPAS

View full text Add to dashboard Cite

show abstract

“…In the past, many efforts have been done to solve fault location problems using intelligent programming techniques [1][2][3][4][5][6][7][8][9][10][11]. Generally, electrical power distribution systems include all parts of electrical utility systems between bulk power supply sources and the consumers' service-entrance equipment.…”

Section: Introductionmentioning

confidence: 99%

Detection of high impedance faults in M.V. mesh distribution network

Mahmoud

2015

2015 Modern Electric Power Systems (MEPS)

View full text Add to dashboard Cite

This paper examines the problem of locating single-line-to-ground faults through resistance that may occur anywhere in an existing multi-ring electrical distribution network that belongs to a Kuwaiti oil fields, in order to improve reliability and to reduce the shutdown time of this network. The developed method is based on using Artificial Neural Network techniques as the algorithm to save time in fault location, as well as estimating the value of the fault resistance. This technique is applied to an existing 13.8-kilovolt-distribution network, which caters to an oil production field spread over an area of approximately sixty km 2 . The nature of this distribution system, which consists of multiring configuration, illustrates the complexity of the network feasibility and the effectiveness of the suggested fault location method. Simulation results for several study cases have shown the feasibility and the effectiveness of the suggested fault location method. Keywords-fault location; single line to ground faults; ANN application; fault resistance, mesh distribution network978-1-5090-3101-6/15/$31.00 ©2015 IEEE

show abstract

“…In order to select the fault line automatically, lots of line selection methods have been proposed. Many of them utilize zero sequence steady state electrical quantities . Because the amplitudes of zero sequence steady state electrical quantities are too small to accurately measure during a single‐phase earth fault, it is difficult to introduce these methods into practical application.…”

Section: Introductionmentioning

confidence: 99%

Phase current fault component based single-phase earth fault segment location in non-solidly earthed distribution networks

Song

et al. 2014

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

SUMMARYA single-phase earth fault section location method based on fault component phase current in non-solidly earthed distribution networks is proposed. During a single-phase earth fault, the fault component currents of three phases obtained from the healthy lines and the sections at the back of the fault point are of the same amplitude and waveform, whereas those differ a lot in the sections between the fault point and the bus. On the basis of this characteristic, a fault section location method is put forward: earth fault detection devices are installed at every section, and each one measures and compares its three phase fault component currents independently, thus the sections between the fault point and the bus are identified, and the fault section location is achieved combining with the network topology. The detailed flow chart and criteria are also given. Simulation results prove the validity and reliability of the method proposed. This novel method requires only current signals, involves a simple principle and a bootstrap characteristic, and is expected in practical application in distribution networks.

show abstract

Transmission line fault location for phase-to-earth fault using one-terminal data

Cited by 34 publications

References 5 publications

Fault Bus Identification in Kurdistan Power Systems using Artificial Neural Network

Fault Bus Identification in Kurdistan Power Systems using Artificial Neural Network

Detection of high impedance faults in M.V. mesh distribution network

Phase current fault component based single-phase earth fault segment location in non-solidly earthed distribution networks

Contact Info

Product

Resources

About