Development and Field-Data Evaluation of Single-Eng Fault Locator for Two-Thermal HVDV Transmission Lines Part I: Data Collection System and Field Data

Ando, M.; Schweitzer, Edmund O.; Baker, R.

doi:10.1109/tpas.1985.318905

Cited by 27 publications

(3 citation statements)

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“…Analysis of single-end measurement-based TW schemes was reported in the literature [26][27][28]. However, the requirement of a large sampling rate for data extraction so as to correctly acquire the wave front arrival time is a major disadvantage of this approach [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Mining Techniques Based Relaying Support for Symmetric-Monopolar-Multi-Terminal VSC-HVDC System

Pragati

Gadanayak

Parida

et al. 2023

ASI

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Considering the advantage of the ability of data-mining techniques (DMTs) to detect and classify patterns, this paper explores their applicability for the protection of voltage source converter-based high voltage direct current (VSC-HVDC) transmission systems. In spite of the location of fault occurring points such as external/internal, rectifier-substation/inverter-substation, and positive/negative pole of the DC line, the stated approach is capable of accurate fault detection, classification, and location. Initially, the local voltage and current measurements at one end of the HVDC system are used in this work to extract the feature vector. Once the feature vector is retrieved, the DMTs are trained and tested to identify the fault types (internal DC faults, external AC faults, and external DC faults) and fault location in the particular feeder. In the data-mining framework, several state-of-the-art machine learning (ML) models along with one advanced deep learning (DL) model are used for training and testing. The proposed VSC-HVDC relaying system is comprehensively tested on a symmetric-monopolar-multi-terminal VSC-HVDC system and presents heartening results in diverse operating conditions. The results show that the studied deep belief network (DBN) based DL model performs better compared with other ML models in both fault classification and location. The accuracy of fault classification of the DBN is found to be 98.9% in the noiseless condition and 91.8% in the 20 dB noisy condition. Similarly, the DBN-based DMT is found to be effective in fault locations in the HVDC system with a smaller percentage of errors as MSE: 2.116, RMSE: 1.4531, and MAPE: 2.7047. This approach can be used as an effective low-cost relaying support tool for the VSC-HVDC system, as it does not necessitate a communication channel.

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Section: Literature Reviewmentioning

confidence: 99%

Data-Mining Techniques Based Relaying Support for Symmetric-Monopolar-Multi-Terminal VSC-HVDC System

Pragati

Gadanayak

Parida

et al. 2023

ASI

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“…One of the methods proposed for AT-detection called the differentiator-smoother algorithm is presented in [9][10][11]. In [12][13][14], this algorithm is applied to conduct the singleended traveling wave-based fault location (SETWFL) and the double-ended traveling wave-based fault location (DETWFL) in transmission lines.…”

Section: Literature Surveymentioning

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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“…As compare to impedance method, travelling wave method would give accurate results [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. The drawback of this method is depending upon system parameters and configuration of network [30][31].…”

Section: Introductionmentioning

confidence: 99%

Location of Downed or Broken Power Line Fault Not Touching the Ground

Nuthalapati¹,

Sinha²

2019

EJEE

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Faults occurred in power system are common, all these faults are detected by protective devices but faults like Downed or Broken power line Fault not touching the ground, these faults not having enough faults current to operate fault detection devices in Over Head power distribution. Till now there is no protecting devices are identified this type of fault and location of this fault. As of now identifying by only by visual sighting. The proposed solution for finding Fault location Downed or Broken power line Fault not touching the ground.

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Development and Field-Data Evaluation of Single-Eng Fault Locator for Two-Thermal HVDV Transmission Lines Part I: Data Collection System and Field Data

Cited by 27 publications

References 1 publication

Data-Mining Techniques Based Relaying Support for Symmetric-Monopolar-Multi-Terminal VSC-HVDC System

Data-Mining Techniques Based Relaying Support for Symmetric-Monopolar-Multi-Terminal VSC-HVDC System

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

Location of Downed or Broken Power Line Fault Not Touching the Ground

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