A novel traveling wave arrival time detection method in power system

Rezaei, Davod; Gholipour, Mehdi; Parvaresh, Farzad

doi:10.1049/gtd2.12357

Cited by 5 publications

(3 citation statements)

References 47 publications

(78 reference statements)

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“…The number 4 defines the wavelet coefficients. It is normally considered the frequency of 40 to 80 KHZ to extract the transient in the wavelet method, whereas the original signal is divided into further frequency bands for wavelet transformation [35].…”

Section: Wavelet Decomposition For Fault Locationmentioning

confidence: 99%

A new algorithm for fault location in multi-end underground cables using traveling waves

Tariq

Alhamrouni

Rehman³

et al. 2023

IJPEDS

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<p>A quick, reliable, and accurate fault location approach is essential in underground power systems protection. Being the most optimistic research topic of electrical power systems, this article presents an optimized algorithm for fault location in multi-end underground cables using travelling waves. Existing algorithms based on wavelet theory have fewer reliability and accuracy issues that raise an error to the power systems in fault location. The proposed layout presents a multiterminal underground cables system where the entire system is segregated into several fault identification sections where this model identifies the faulty section and the faulty half. Voltage and current wave transient at the mid-point of each fault locator are taken into account to eliminate the time-synchronous error. Traveling waves models are modelled using Bewley diagrams. Detecting the first and second traveling wave transient at both ends of each cable keeps the system reliable. Extensive simulations are simulated using the alternative transient program (ATP) to discriminate, identify, and locate several faults, while the system can also validate the faults near the busbar. The model is developed in MATLAB, and the obtained results depict the proposed algorithm's higher accuracy in fault location.</p>

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Section: Wavelet Decomposition For Fault Locationmentioning

confidence: 99%

A new algorithm for fault location in multi-end underground cables using traveling waves

Tariq

Alhamrouni

Rehman³

et al. 2023

IJPEDS

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“…The method proposed in [32] is based on the expression of the traveling wave as the exponentially damped component superimposed on the sinusoidal wave in a small time window. The sine component is removed by using approximation and consecutive differences of the input signal samples from the resulting wave in time domain.…”

Section: Literature Surveymentioning

confidence: 99%

“…In this case, to extract the TWATs from the noisy signal, the length of the windows is selected as (32,16). Quadrupling the length of the windows in noisy conditions caused one microsecond difference in TDOAs compared to the no-noise mode.…”

Section: The Effect Of Windows Lengthmentioning

confidence: 99%

A new traveling‐wave detection method for fault location based on two sequential sliding windows and curve fitting

Rezaei

Gholipour

Parvaresh

2022

IET Generation Trans & Dist

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The basis of traveling wave-based fault location methods is to extract the arrival times of transient signals in the power network. In this paper, a new method for extracting the traveling wave arrival times is presented. For this purpose, the aerial modes of three-phase voltage signals are extracted and two sequential sliding windows of unequal length move along with this signal. By fitting a line to the samples inside each of the two windows and calculating the angle between them, the traveling wave arrival times can be determined with high accuracy. Because this takes place in the time domain, there is no need to switch between time and frequency domains similar to those in Fourier transforms. On the other hand, fitting curves reduce the negative effects of noise and sampling frequency changes. EMTP-ATP is applied to perform the transient simulations and the results are then analysed in MATLAB to conduct the sensitivity analysis against the measurement noises, the sampling frequency, and the fault parameters. The proposed technique is compared to the common techniques such as discrete wavelet transforms and Hilbert Huang transforms. The results demonstrate that the proposed technique has acceptable performance and covers the drawbacks of common methods. INTRODUCTIONThe motivation for addressing the issue of traveling wave arrival time (TWAT) detection, the work done in this field, and what is discussed in this article can be summarized as follows. MotivationAccurate fault location in transmission lines reduces power system recovery time, associated costs, and financial losses, especially in deregulated environments. Fault location in the power grid is done in three ways; impedance-based methods [1, 2], traveling wave-based fault location (TWFL) [3-5], and artificial intelligence-based methods [6, 7]. The impedancebased method use power frequency components of voltages and currents while the traveling wave-based methods adopt high-frequency transient components generated by the fault or switching operations. Artificial intelligence/machine learning (AI/ML) methods are generally based on soft computing.In recent decades, with the advancement of signal processing and the possibility of signal sampling at high frequencies,This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Novel wavefront detection and fault location method based on Hilbert-Huang transform for long HVDC transmission lines

Zhu

Li²,

Guo³

et al. 2022

Electric Power Systems Research

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A novel traveling wave arrival time detection method in power system

Cited by 5 publications

References 47 publications

A new algorithm for fault location in multi-end underground cables using traveling waves

A new algorithm for fault location in multi-end underground cables using traveling waves

A new traveling‐wave detection method for fault location based on two sequential sliding windows and curve fitting

Novel wavefront detection and fault location method based on Hilbert-Huang transform for long HVDC transmission lines

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