Fault detection and classification in electrical power transmission system using artificial neural network

Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

doi:10.1186/s40064-015-1080-x

Cited by 194 publications

(76 citation statements)

References 16 publications

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“…Such backup system plays a crucial role during power grid fault conditions and protection system breakdowns [1][2][3][4][5]. Hence, such methodologies are gaining much research attraction in recent years [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements

Gopakumar

Mallikarjuna

Reddy

et al. 2018

Prot Control Mod Power Syst

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Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns. This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid. In contradiction to conventional methods, transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit (PMU) at one of the generator buses. This minimal requirement makes the proposed methodology ideal for providing backup protection. Spectral analysis of equivalent power factor angle (EPFA) variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid. Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence. The proposed system can considerably assist system protection center (SPC) in fault localization and to restore the line at the earliest. Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.

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Section: Introductionmentioning

confidence: 99%

Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements

Gopakumar

Mallikarjuna

Reddy

et al. 2018

Prot Control Mod Power Syst

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“…Hence, fault is need to be detected instantly to prevent the problem to happen. In determining the fault location, one of the most essential thing is fault type classification [9]- [13].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, many methods to identify the fault types have been introduced by the researchers in [3], [9]- [14] which are Artificial Neural Network and Fuzzy Logic. Artificial Neural Network is widely used for its programming technique to solve the non-linear issues and also the problem with fault detection and type classification in electrical power system [9].…”

Section: Introductionmentioning

confidence: 99%

Fault Types Classification Accuracy Considering Four Different Methods

Awalin¹,

Naidu²,

Mokhlis³

et al. 2018

Preprint

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This research introduces four different tools designed for fault type classifications at distribution network. The proposed designs are using Artificial Neural Network, Fuzzy Logic, conventional method and Support Vector Machine as the research techniques with input data obtained from PSCAD simulation. The circuit configuration for fault disturbance at the distribution network was simulated by PSCAD simulation program. The research techniques were applied with multiples input values of voltage and current that extracted from the PSCAD simulation. This research testifies the output result by using different fault resistance values; 0.01Ω, 10Ω, 30Ω, 50Ω and 70Ω. Voltage sag and current swell of phase a, b and c that were obtained from the PSCAD simulation have been used as the input variables for the four different research tools design. The acquired results that represented in average accuracy (%) shows that voltage sag and current swell can draw a satisfying accuracy in classifying the fault type.

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“…The information contained in the waves can be extracted and used to detect and locate faults. In this paper, the digital techniques which use the wavelet theory to detect and classify faults on transmission lines are proposed [12]. …”

Section: Transmission Line Protectionmentioning

confidence: 99%

Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network

Thwe¹

2016

JEEE

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Abstract:The artificial neural network is a powerful tool for the detection of the transmission line faults due to its ability to differentiate between various patterns. This paper deals with the application of artificial neural networks (ANNs) to the fault detection and classification in high voltage transmission lines for high speed protection which can be used in digital power system protection. The three phase currents of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. The ANN was trained and tested using various sets of field data, which was obtained from the simulation of faults at various fault scenarios (fault types, fault locations and fault resistance) of 230 kV, 193.2 km in length "Mansan-Shwesaryan, Mandalay Region, Myanmar" transmission line using a computer program based on MATLAB/Simulink. Simulation results confirm that the proposed method can efficiently be used for accurate fault classification on the transmission line.

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Fault detection and classification in electrical power transmission system using artificial neural network

Cited by 194 publications

References 16 publications

Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements

Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements

Fault Types Classification Accuracy Considering Four Different Methods

Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network

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