A novel algorithm to determine fault location in a transmission line using PMU measurements

Venugopal, Mahesh; Tiwari, Chandrakant

doi:10.1109/icsima.2013.6717957

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…We highlight anomaly detection and classification [25,26], social media such as Twitter in [27], the estimation of active ingredients such as PV installations [28,29] and finally the real-time data for online transient stability evaluation [30]. In addition, we point out researchs about the improvement in wide-area monitoring, protection and control (WAMPAC) and the utilization of PMU data [31][32][33][34], together with the fault detection and location [35][36][37]. Xie et al, based on Principal component analysis (PCA), proposes an online application for early event detection by introducing a reduced dimensionality [38].…”

Section: B the Role Of Data In Future Power Gridmentioning

confidence: 99%

Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory

Mouftah

Erol‐Kantarci²,

Rehmani³

2018

Transportation and Power Grid in Smart Cities

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A cornerstone of the smart grid is the advanced monitorability on its assets and operations. Increasingly pervasive installation of the phasor measurement units (PMUs) allows the so-called synchrophasor measurements to be taken roughly 100 times faster than the legacy supervisory control and data acquisition (SCADA) measurements, time-stamped using the global positioning system (GPS) signals to capture the grid dynamics. On the other hand, the availability of low-latency two-way communication networks will pave the way to high-precision real-time grid state estimation and detection, remedial actions upon network instability, and accurate risk analysis and post-event assessment for failure prevention.In this chapter, we firstly modelling spatio-temporal PMU data in large scale grids as random matrix sequences. Secondly, some basic principles of random matrix theory (RMT), such as asymptotic spectrum laws, transforms, convergence rate and free probability, are introduced briefly in order to the better understanding and application of RMT technologies. Lastly, the case studies based on synthetic data and real data are developed to evaluate the performance of the RMT-based schemes in different application scenarios (i.e., state evaluation and situation awareness).

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Section: B the Role Of Data In Future Power Gridmentioning

confidence: 99%

Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory

Mouftah

Erol‐Kantarci²,

Rehmani³

2018

Transportation and Power Grid in Smart Cities

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“…The determination of fault location work in a transmission line using synchronized measurements can also be done by considering variation in source and load side impedance. 43 This algorithm works for symmetrical as well as unsymmetrical faults and is also less dependent on CT. A PMU-based dynamic fault location estimation for transmission lines is another method which apply PMU data for fault location. 44 This method considers the effect of dynamic variations like power oscillation on transmission lines parameters.…”

Section: Advanced Review Wireswileycom/energymentioning

confidence: 99%

A review of synchrophasor applications in smart electric grid

Lee

Tushar

Cui

et al. 2016

WIREs Energy & Environment

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The adoption of synchrophasor technology with ongoing smart grid activities has resulted in a transformation of the power system monitoring and control practices. Phasor measurement units (PMUs) synchronized by the global positioning system (GPS) provide time‐stamped voltage and current phasors at faster rate for enhanced situational awareness and decision support compared to existing legacy technology and help in improving the reliability of the power system. The measurements from PMU can be used for number of possible applications and can be classified based on the time criticality requirements (e.g., adaptive protection vs state estimation (SE)), control center versus engineering analysis (e.g., state estimation vs model validation), monitoring versus control (e.g., angle monitoring vs stability control), and existing versus evolving (e.g., state estimation vs transient stability) applications. Some of these synchrophasor applications like monitoring and postmortem studies have been already adopted by power industry, some of the synchrophasor‐based control applications are still being investigated. In this study, various power system applications supported by synchrophasor technology are reviewed and discussed. WIREs Energy Environ 2017, 6:e223. doi: 10.1002/wene.223 This article is categorized under: Energy Systems Economics > Systems and Infrastructure

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“…This method will be more precise than single-terminal methods due to their dependence on the fault resistance and type of fault. [9][10] Along with the existing methods, learning algorithm-based methods can also be used as an alternative to locating faults in transmission lines. Learning-based strategies, if executed properly in different conditions and despite the certainty in the system, can show acceptable flexibility and performance.…”

Section: Introductionmentioning

confidence: 99%

Fault Location in the Transmission Network Using a Discrete Wavelet Transform

Dashtdar¹

2019

AJECE

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In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current signal in order to faultlocation evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault).The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.

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A novel algorithm to determine fault location in a transmission line using PMU measurements

Cited by 7 publications

References 16 publications

Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory

Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory

A review of synchrophasor applications in smart electric grid

Fault Location in the Transmission Network Using a Discrete Wavelet Transform

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