Two-terminal fault detection and location for hybrid transmission circuit

Idris, Muhd Hafizi; Adzman, Mohd Rafi; Mokhlis, Hazlie; Tajuddin, Mohammad Faridun Naim; Hamid, Haris Hafizal Abd; Amirruddin, Melaty

doi:10.11591/ijeecs.v23.i2.pp639-649

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…However, the authors mention that this may not be the case if applied using other interference location methods. While the paper Idris et al [12] has also examined the location of interference by using data from two different terms to detect interference in the transmission system. Awalin et al [13] focuses on how to detect the types of disturbances that occur in electrical power distribution systems.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the lowest value of each figure may be compared again to make it simpler to draw conclusions in choosing the optimum placement for PMUs. The following equation compares the three lowest values from Figures 5 to 7: PMU 1 (0.0093) < PMU 23 (0.0095) < PMU 30 (0.0097)(12). According to the comparison of the lowest MSE value based on equation 12, the least value of the overall MSE calculation is PMU 1of 0.0093.…”

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confidence: 99%

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Wide area monitoring system control management of the IEEE-14 bus system using least square support vector regression

Awalin

Halim

Ramli

et al. 2022

IJEECS

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The <span>wide area monitoring system (WAMS) records and monitors every fault or disturbance that occurs in a power system network using phasor measuring units (PMUs). Extensive monitoring of the condition of the electrical power system can ensure the sustainability of reliable energy. The accuracy of the PMUs placement can be determined using the least square support vector regression (LS-SVR) technique. The primary goal of this study is to assess the level of accuracy of the PMUs placement using mean square error (MSE). First, the IEEE-14 bus system equipped with PMUs was built in Matlab software using Simulink. The MSE of the PMUs was then calculated using the LS-SVR. The results revealed that the lower the MSE, the better the PMUs placement. It was also observed that placing the PMUs on bus 2, bus 6, and bus 9 produced the lowest value of MSE</span>.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Wide area monitoring system control management of the IEEE-14 bus system using least square support vector regression

Awalin

Halim

Ramli

et al. 2022

IJEECS

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“…Conventional fault locations use the impedance [2]- [6] and travelling wave [7]- [9] methods. In impedance-based methods, the distance of the fault location is proportional to the impedance between the fault point and measurement terminal.…”

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confidence: 99%

Neural Network based Transmission Line Fault Classifier and Locator using Sequence Values

Idris¹,

Adzman²

2023

J. Phys.: Conf. Ser.

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Faults can easily occur at transmission line because the line is exposed to the environment. The fast fault location after a fault occurrence will minimize the time to repair the faulty part thus reduce the stress of power system due to long outage time. This paper demonstrates the development of fault classifier and fault locator using neural network. The positive, negative and zero sequence values of three-phase voltage and current during fault time were used as the inputs to train the neural networks. Various fault conditions which have different fault types, fault resistance values and fault locations have been simulated to generate the training data for both neural network based fault classifier and fault locator. From the results, the fault classifier and locator successfully classified and located all the simulated fault conditions. One of the advantages of using sequence values as the inputs for the neural networks is only one fault type need to be simulated and used as the training data for single line-to-ground fault (RG, YG and BG), line-to-line fault (RY, YB, BR) and double line-to-ground fault (RYG, YBG, BRG).

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Arcing fault diagnosis using enhanced cross-correlation technique

Amirruddin

Adzman

Affendi

et al. 2022

J. Phys.: Conf. Ser.

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This study explored the potential use of cross-correlation as a technique for detecting arcing faults in a power system distribution network. The cross-correlation technique was employed to investigate the effect of each antenna placement as a detection device, time difference of arrival (TDOA), time delay, and correlation magnitude of arcing signals detected during on-line arcing fault measurement. The arcing fault was detected using four antennas that had been set up around the arc source point in a high voltage (HV) laboratory. The measurements were taken using a digital oscilloscope. For precise results, the Discrete Wavelet Transform (DWT) denoising technique combined with cross-correlation (CC) technique were applied using MATLAB software to identify the arcing signals detected in order to diagnose the differentiation between noisy and real arcing fault signals. Further assessment was carried out by performing a cross-correlation technique on the real arcing signals obtained to find the similarities and arrival time’s delay between single arcing signals’ placement. The outcome shows that all measurements including the time difference of arrival (TDOA), correlation magnitude, time delay, and antennas’ placement towards the arcing source point are valuable in determining the arcing signals detected precisely.

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Two-terminal fault detection and location for hybrid transmission circuit

Cited by 3 publications

References 22 publications

Wide area monitoring system control management of the IEEE-14 bus system using least square support vector regression

Wide area monitoring system control management of the IEEE-14 bus system using least square support vector regression

Neural Network based Transmission Line Fault Classifier and Locator using Sequence Values

Arcing fault diagnosis using enhanced cross-correlation technique

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