Classification of Single Line to Ground Faults on Double Circuit Transmission Line using ANN

Jain, Ajit; Thoke, A. S.; Patel, R. N.

doi:10.7763/ijcee.2009.v1.30

Cited by 18 publications

(6 citation statements)

References 14 publications

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“…This paper provides further applications for artificial neural networks in transmission lines [23,24,25]. They discuss the potential use of the artificial neural network in detecting and classifying single line and ground faults on circuit transmission lines with remote end infeed [25,26,27]. The problem here is due to the mutual coupling of two circuits during different fault conditions.…”

Section: Review Of Related Literaturementioning

confidence: 99%

ANN Distance Protection for Transmission Lines

Africa¹

2019

IJETER

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When using transmission lines, faults often happen during the flow of signals. One way to protect transmission lines is to use distance protection relays. These types of relays, however, are still affected by fault resistance. Fault conditions often follow a certain pattern. Having a relay that recognizes these patterns can help improve performance. This paper presents a method to implement this using an artificial neural network (ANN). An ANN is one of the many applications of machine learning, which in meaning, is the algorithms that give the ability to computers to obtain knowledge through data and adapt to make certain deductions and choices. This paper will only consider the effect of the fault resistance of a single line-to-ground type of fault. The ANN should be programmed to identify numerous patterns that each correspond to different fault conditions. This will help in identifying and classifying unknown patterns detected in the transmission lines.

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Section: Review Of Related Literaturementioning

confidence: 99%

ANN Distance Protection for Transmission Lines

Africa¹

2019

IJETER

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“…Three-phase voltages and six-line currents are used as input patterns for ANN. It can be used to detect and classify all line-toground fault types, which occurred in three phases of both transmission line circuits which resulting in breakdown the insulator [5][6][7][8][9][10]. The mutual effect problem that occurs in double circuit power system transmission lines causes inaccurate operation of the traditional protection device.…”

Section: Introductionmentioning

confidence: 99%

Improvement of protection relay with a single phase autore-closing mechanism based on artificial neural network

Hussain¹,

Ali²,

Allu³

et al. 2020

IJPEDS

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This paper presents a developed logical tripping scheme to improve conventional protection performance. Adaptive single pole auto reclosure (ASPAR) system is proposed that considers, automatically tripping and reclosing of a multi-shot independent pole technique of a circuit breaker at a predetermined sequence, which can be used to boost the synchronization of the power grid under the transient fault conditions. Moreover, the ASPAR can be utilized to enhance the electrical system stability and reliability at the same operating conditions. Based on the three-phase system, the Artificial neural network (ANN) in this work has been done in order to diagnose and detect healthy and faulted phases. The proposed ANN fault classifier method consists of the logic gates, router circuits, timers, and positive and negative sequence analyses circuit. In addition, it is used to give the ability to recognize a fault type, which by training on the sequence angle values and coordination of the transmission line. Three-phase overhead transmission line including the proposed ASPAR is built in MATLA \SIMULINK environment. Thus the performance ANN-fault classified is tested under different fault conditions. Simulation results show that the proposed ASPAR based on ANN is accurate and well performance. Whereas resultant tripping and reclosing signals of ASPAR are successfully provided that enhances the circuit breaker mechanism under these operating condition.

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“…The proposed neural network architectures suffer from a large number of training cycles and a high computational burden. Another significant drawback for using ANN is that the resolution is not efficient since it can be a very sparse network with the need for large size training data adding an additional burden on its computational complexity [3][4][5][6]. Wavelet transform has been proposed by many to decompose voltage and current waves in an effort to identify a fault.…”

Section: Introductionmentioning

confidence: 99%

Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis

Alsafasfeh¹,

Abdel‐Qader²,

Harb³

2012

EPE

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A vital attribute of electrical power network is the continuity of service with a high level of reliability. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing on fault detection, classification and localization. In this paper, a new protective relaying framework to detect, classify and localize faults in an electrical power transmission system is presented. This work will extract phase current values during ( )th of a cycle to generate unique signatures. By utilizing principal component analysis (PCA) methods, this system will identify and classify any fault instantaneously. Also, by using the curve fitting polynomial technique with our index pattern obtained from the unique fault signature, the location of the fault can be determined with a significant accuracy

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Classification of Single Line to Ground Faults on Double Circuit Transmission Line using ANN

Cited by 18 publications

References 14 publications

ANN Distance Protection for Transmission Lines

ANN Distance Protection for Transmission Lines

Improvement of protection relay with a single phase autore-closing mechanism based on artificial neural network

Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis

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