Review of Transmission Line Fault Location using Travelling Wave Method

Wijaya, Gunawan; Suwarno, &; Abu-Siada, A.

doi:10.1109/icpere.2018.8739752

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…These waves spread along the line and experience reflections and refractions as they propagate. Then the energy of these traveling waves is gradually reduced according to the fault and system characteristics until it is completely dissipated [22]. These waves contain information about the fault, including its location and its distance from the system terminals, which can be used to detect and locate the faulty point.…”

Section: Travelling Wave-based Fault Location Theorymentioning

confidence: 99%

Artificial Neural Network-based Fault Location in Terminal-hybrid High Voltage Direct Current Transmission Line

Hadaeghi¹,

Iliyaeifar²,

Chirani

2023

IJE

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In this article, a fault location technique based on artificial neural networks (ANN) for Terminal-Hybrid LCC-VSC-HVDC has been assessed and scrutinized. As is known, in conventional HVDC systems (LCC-based and VSC-based HVDCs), the same type of filter is used on both sides due to the use of similar converters in both sender and receiver terminals. In this article, it is concluded that due to the use of two different types of converters at the both ends of the utilized Terminal-hybrid LCC-VSC-HVDC system, and the use of different DC filters on both sides, fault location using positive and negative pole currents of the rectifier side has much better results than the rest of input signals. Therefore, it will be finalized that by increasing and designing suitable DC filters on the transmission line of HVDC systems, fault localization matter will be remarkably and surprisingly facilitated. Nowadays, the fault location of HVDC transmission lines with a value of more than 1% is generally discussed in most articles. In this research, the fault location with a value of 0.0045%, i.e., a distance of 22.5 meters from the fault point in the most satisfactory case is obtained, which shows the absolute feasibility of the ANN along with the wavelet transform. To validate the proposed method, a ±100 KV, Terminal-hybrid LCC-VSC-HVDC system is simulated via MATLAB. The outcomes verify that the proposed technique works perfectly under various fault locations, resistances, and fault types.

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Section: Travelling Wave-based Fault Location Theorymentioning

confidence: 99%

Artificial Neural Network-based Fault Location in Terminal-hybrid High Voltage Direct Current Transmission Line

Hadaeghi¹,

Iliyaeifar²,

Chirani

2023

IJE

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“…Whenever a fault occurs, these waves propagate through the transmission line and reflect back at the two transmission line terminals. The identification of these waves and measurement of their arrival times, associated with the speed propagation of the transmission lines, provide accurate results [12]. This method can be divided into two groups [3], [13]: passive and active methods.…”

Section: Introductionmentioning

confidence: 99%

Fault Location in Overhead Transmission Lines Based on Magnetic Signatures and on the Extended Kalman Filter

Neto¹,

Sartori

Manassero

2021

IEEE Access

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This paper proposes a non-contact method for fault location in transmission lines, which is based on magnetic fields produced by current signals measured using magnetoresistive sensors installed only at transmission line terminals, under the phase conductors of the first transmission tower at both terminals or at the substations portals. The proposed method uses the Extended Kalman filter to process these measurements and is based on a travelling wave approach in order to perform the fault localization. This paper also describes the implementation and testing of the method, firstly introducing its overview, followed by an analysis of the magnetic fields produced by the current signals, as well as considerations on their measurement; secondly, detailing the Extended Kalman filter and the travelling wave approach; and, finally, presenting the results of the method with regards to simulations built using EMTP/ATP to evaluate its robustness under different conditions varying the fault resistance, fault inception angle, phases involved and fault location. The results indicate that the proposed method is robust and accurate.

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“…However, this method can be affected by various power system conditions and parameters such uncertainty of zero-sequence values, fault resistance, remote infeed current, source impedance etc [10]. For travelling wave methods, the time the fault wave travel from the fault point and reflect back to the measurement terminal is used to estimate the fault location [11]. Some of the drawbacks of the travelling wave methods are such as the propagation of the wave can be affected by the network configuration and system parameters and the difficulty to determine the fault near the bus [12].…”

mentioning

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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Review of Transmission Line Fault Location using Travelling Wave Method

Cited by 8 publications

References 15 publications

Artificial Neural Network-based Fault Location in Terminal-hybrid High Voltage Direct Current Transmission Line

Artificial Neural Network-based Fault Location in Terminal-hybrid High Voltage Direct Current Transmission Line

Fault Location in Overhead Transmission Lines Based on Magnetic Signatures and on the Extended Kalman Filter

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

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