Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements

Gopakumar, Pathirikkat; Mallikarjuna, Balimidi; Reddy, M. Jaya Bharata; Mohanta, Dusmanta Kumar

doi:10.1186/s41601-018-0089-x

Cited by 45 publications

(17 citation statements)

References 25 publications

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“…The PMU usage increased in 2004 when the U.S.-Canada investigation report of the 2003 blackout was released. The blackout report recognized that many of North America's major blackouts have been caused by inadequate situational awareness for grid operators and recommended the use of synchrophasor technology to provide real-time wide-area grid visibility [15], [16]. It was demonstrated that new technology and investment was needed to provide adequate situational awareness and the ability to react to such a major disturbance.…”

Section: Synchrophasor Data Description a Related Work Of Synchmentioning

confidence: 99%

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Zhang

2020

IEEE Access

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Deep learning techniques have been widely used for power system operations as a very hot topic in recent years. This paper proposed a spatiotemporal deep learning-based low frequency oscillation mode estimation method using synchrophasor data. The proposed deep learning method consists of the graph convolutional network (GCN) and the long short-term memory (LSTM). A graph network is used to model the power network in which the edges represent the connection relationships between system buses. Specifically, the GCN method is used to capture the topological structure of the power networks to obtain the spatial dependence. The LSTM model is used to capture the dynamic change of electrical variables that can be monitored by PMU devices through synchrophasor data to obtain the temporal dependence. Eventually, the proposed GCN-LSTM model is used to capture the spatiotemporal patterns and features of the system-wide synchrophasor data. To validate the effectiveness of the proposed method, we evaluate it on two classic IEEE simulation platform, i.e., IEEE 39-bus system and IEEE 118-bus system, by comparing with the Nonlinear Autoregressive Neural Network with External Input (NARX), the Gated Recurrent Unit (GRU) network, and single LSTM methods. It is demonstrated that the proposed GCN-LSTM method can achieve the best estimation results for different simulation platforms.

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Section: Synchrophasor Data Description a Related Work Of Synchmentioning

confidence: 99%

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Zhang

2020

IEEE Access

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“…Fault prediction is accomplished by Rajaraman et al using the zero sequence, negative sequence and positive sequence of currents [12]. The frequency coefficients of equivalent power factor angle variation and nominal voltage coefficients are used as inputs to the support vector machine (SVM) classifier for predicting the type of fault in reference [13]. A synchrophasor assisted protection scheme for the protection of a long transmission line compensated with the shunt FACTS device is presented in reference [14].…”

Section: B Related Workmentioning

confidence: 99%

Expeditious Situational Awareness-Based Transmission Line Fault Classification and Prediction Using Synchronized Phasor Measurements

2019

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The wide area situational awareness attempts at the expeditious detection of imminent system abnormalities and alerting system operators to take appropriate measures. Because the critical situation may arise in a system due to faults on transmission lines spanning over a long distance, phasor measurement units (PMUs) have become an indispensable measuring device to provide a dynamic view of such a wide area system. In this paper, the perception about a 200 km long transmission line has been achieved with the help of phasor measurements from PMU, which has the capability of reporting 200 phasors per second. The comprehension about the perceived event is accomplished by computing the deviations of current phasor magnitude as well as phase angles derived from synchronized phasor measurements using the phaselet algorithm. Based on the comprehension of the perceived event, a specific type of fault has been predicted using the Gaussian Naïve Bayes approach. In order to validate the proposed methodology, it has been implemented on a laboratory setup.

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“…SA gives an estimate of the power system operator's ability to perceive and take commensurate control actions. From the power system perspective, SA is defined as the perception of the power system by measuring various parameters, comprehending the data obtained from the components for improved SA, and prognosis of the situation of the system depending on the interpretation of the data captured [3,4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme

Swain

Cherukuri

2021

SN Appl. Sci.

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The power system stability and reliability are stimulated by the faults on the transmission line. Many researchers have explored the performance of the transmission system under various kinds of faults. Specifically, the arrival of expeditious and effective data acquisition systems with high rate of sampling has set down the foundation for successful real-time monitoring. Using the LabVIEW and the data acquisition system’s of National Instruments (NI), virtual systems have been developed for obtaining optimal paradigmatic data with appropriate characterization and quality transmission. The primary objective of the work is to perceive and comprehend the transmission line faults with the aid of synchronized phasor measurements obtained from the phasor measurement unit (PMU) as well as protecting the system using auto-reclosing signal. The developed algorithms include phaselet coefficients for perception as well as comprehension. In order to increase the accuracy, particle swarm optimized extreme learning machine technique has also been used for comprehension. A protection scheme is employed using auto-reclosing to minimize the power loss and quick reconnection the power line in case of temporary fault. Developed algorithms have been validated on a practical laboratory transmission line using NI PMU. As the LabVIEW platform has been used for simulations, it is composed of visual displays such that the system operator can efficiently perform the planning and control decisions.

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Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements

Cited by 45 publications

References 25 publications

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Expeditious Situational Awareness-Based Transmission Line Fault Classification and Prediction Using Synchronized Phasor Measurements

Intelligent fault analysis of transmission line using phasor measurement unit incorporating auto-reclosure protection scheme

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