Extract Fault Signal via DWT and Penetration of SVM for Fault Classification at Power System Transmission

Zakri, Azriyenni Azhari; Darmawan, Syukri; Usman, Jafaru; Rosma, Iswadi Hasyim; Ihsan, Boy

doi:10.1109/icon-eei.2018.8784320

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The coordinate for each fault type was designed to predict the fault location coordinates in the form of latitude and longitude in single-phase short circuit to ground (AG, BG, CG), 2-phase to ground (ABG, ACG, BCG), inter-phase (AB, AC, BC), and 3-phase (ABC) [5]. Figure 2 presents a block diagram of ANN to estimate the coordinates of the fault location, this is intended to estimate the coordinates of the type of fault in.…”

Section: Ann Design For the Coordinate Point Of Fault Locationmentioning

confidence: 99%

“…Several methods have been applied to determine the location of faults on a power transmission line and an example of these methods is the Phasor Measurement Unit (PMU) which is part of the current and voltage values in the control center while the application system uses IEEE 9-bus [3,4]. Some other hybrid techniques which have been implemented include Discrete Wavelet Transformation (DWT) and Support Vector Machine (SVM) [5,6] as well as several others applied in electric power transmission lines. Moreover, fault diagnosis has been monitored in Wide Area System using the Phasor Measurement Unit (PMU) with measurements conducted by monitoring, controlling, and protecting as well as through the combination of the functions of the phasor measuring device [4,7] while the fault in hybrid electrical network is usually detected based on GPS and PMU [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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The Estimate of Fault Location based on Transmission Tower Coordinates

2022

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This study was conducted to predict the fault location based on tower coordinates using the Artificial Neural Network (ANN). The electrical power system modeled made use of an actual system including 150 kV transmission line with KP bus to the GS bus and 64 km length while ANN technique was used to coordinate the points for the fault location on the electric power transmission line due to its ability to predict what will happen in the future based on the pattern of past events. The ANN was tested and trained at certain iterations with different data which were simulated for short circuit fault type at several locations to achieve the best value. The results obtained with fault location coordinates were in the form of latitude and longitude while the simulation results for the AG fault were at 0.0381 km distance starting from the KP bus to the GS bus at 8,246 kA. Moreover, the estimated error values were found in the 2-phase fault to the ground at 4.01×10- 3 % while the ANN structure performance showed the MSE value to be 2.08 - 4 % with a very small error value of 0.57 % and this means it is included in the existing standard tolerance category. The data validation of this system modeling was conducted on the short circuit current value between simulation and theoretical estimation.

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Section: Ann Design For the Coordinate Point Of Fault Locationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Estimate of Fault Location based on Transmission Tower Coordinates

2022

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“…In [2], variational mode decomposition has been used for detecting faults in transmission line. In [3], DWT (discrete wavelet transform) and SVM (support vector machine) have been utilized for fault detection and classification in transmission line. K-nearest neighbour has been used for parallel TLs [4].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Evolving Faults in Two-Terminal Series Compensated Transmission Line Using DMWT

Gupta¹,

Rahul²

2020

SKIT Research Journal

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This paper proposes a discrete Meyer wavelet transform (DMWT)-based evolving faults recognition system for the two-terminal series capacitor compensated transmission line (TTSCCTL). The particular region captured currents of the TTSCCTL are used to estimate the DMWT outputs. To authorize the performance of the DMWT, simulation studies have been done thus varying fault type, resistance, and switching time. The main advantage of DMWT is that it correctly recognizes all types of evolving faults in TTSCCTL by employing one-side fault current data only. It is also investigated that the proposed technique is robust to the deviation in the input fault parameters of TTSCCTL.

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Fault Location in Transmission Line Through Deep Learning—A Systematic Review

Kanagasabapathy¹

2021

Inventive Systems and Control

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Extract Fault Signal via DWT and Penetration of SVM for Fault Classification at Power System Transmission

Cited by 4 publications

References 10 publications

The Estimate of Fault Location based on Transmission Tower Coordinates

The Estimate of Fault Location based on Transmission Tower Coordinates

Recognition of Evolving Faults in Two-Terminal Series Compensated Transmission Line Using DMWT

Fault Location in Transmission Line Through Deep Learning—A Systematic Review

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