Phasor Measurement Unit Test Under Interference Conditions

Ghiga, Radu; Martin, Kenneth E.; Wu, Qiuwei; Nielsen, Arne Hejde

doi:10.1109/tpwrd.2017.2691356

Cited by 27 publications

(12 citation statements)

References 33 publications

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“…Therefore, for verifying the robustness of the proposed model to the noisy data, Gaussian noise is added to the data generated through simulation. This Gaussian noise is also known as Additive white Gaussian noise (AWGN), which is frequently used to replicate the background noise of the system under study 62 . The signal‐noise‐ratio (SNR) value of PMU measurements can vary due to different electrical systems and regions 63 .…”

Section: Resultsmentioning

confidence: 99%

Classification of faults in an IEEE 30 bus transmission system using fully convolutional network

Tikariha

Londhe

Bag

et al. 2021

Int Trans Electr Energ Syst

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Summary Fault diagnosis is vital for detecting the type of fault and rapid restoration of the electric power supply. The existing machine learning‐based classification method for detecting fault shows better performance. But these methods require manual pre‐processing steps and feature engineering, which makes them unreliable and time‐consuming for the complex and multi‐bus system. In this paper, a fully automated approach based on a deep learning framework has been proposed for the multi‐classification of faults in a grid network. The deep learning framework provides a better incentive to predict the type of fault directly using raw data as input. For this intended task of fault diagnosis, a fully convolutional network is designed and implemented to provide better performance with less trainable parameters. The proposed scheme is tested on the large number of simulated data generated from an IEEE 30 bus system. The performance for fault classification is validated through 10‐fold cross‐validation and numerous performance metrics like accuracy, recall, precision, specificity, and F1 score are evaluated. The performance of the different models with parameter variation is compared to obtain better performance with optimal trainable parameters. It has also been compared with some existing methods and shows promising result for fault classification in the large or multi‐bus system.

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Section: Resultsmentioning

confidence: 99%

Classification of faults in an IEEE 30 bus transmission system using fully convolutional network

Tikariha

Londhe

Bag

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…2) Impact of Noise on Algorithm Performance: Table II records the localization accuracy under different levels of noise. We can conclude the proposed algorithm performs well under the cases with signal-to-noise ratio (SNR) less than 30 dB which is lower than the SNR used for PMU-related tests [18].…”

Section: B Algorithm Robustnessmentioning

confidence: 86%

A Synchrophasor Data-Driven Method for Forced Oscillation Localization Under Resonance Conditions

Huang

Freris

Xie

2020

IEEE Trans. Power Syst.

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This paper proposes a data-driven algorithm of locating the source of forced oscillations and suggests the physical interpretation of the method. By leveraging the sparsity of the forced oscillation sources along with the low-rank nature of synchrophasor data, the problem of source localization under resonance conditions is cast as computing the sparse and lowrank components using Robust Principal Component Analysis (RPCA), which can be efficiently solved by the exact Augmented Lagrange Multiplier method. Based on this problem formulation, an efficient and practically implementable algorithm is proposed to pinpoint the forced oscillation source during real-time operation. Furthermore, we provide theoretical insights into the efficacy of the proposed approach by use of physical model-based analysis, in specific by establishing the fact that the rank of the resonance component matrix is at most 2. The effectiveness of the proposed method is validated in the IEEE 68-bus power system and the WECC 179-bus benchmark system.

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“…After the achievement of Msens1 (~10 4 %/mT) and Mdetc1 (~1 pT/Hz 0.5 ) in ~2027, the implementation of the large-scale power grid monitoring systems with MR sensors (TRL 7-8) will be expected. The full establishment of these systems (TRL 8-9) will require a large quantity of supporting facilities (e.g., energy harvesting for outdoor sensors [415,416], and a common time source for synchronized measurements [417,418]), and therefore will be realized in a long-term period (after ~2027).…”

Section: Non-destructive Evaluation and Monitoring (Ndem)mentioning

confidence: 99%

Magnetoresistive Sensor Development Roadmap (Non-Recording Applications)

et al. 2019

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Magnetoresistive (MR) sensors have been identified as promising candidates for the development of high-performance magnetometers due to their high sensitivity, low cost, low power consumption, and small size. The rapid advance of MR sensor technology has opened up a variety of MR sensor applications. These applications are in different areas that require MR sensors with different properties. Future MR sensor development in each of these areas requires an overview and a strategic guide. A MR sensor roadmap (non-recording applications) was therefore developed and made public by the Technical Committee of The IEEE Magnetics Society with the aim to provide an R&D guide for MR sensors intended to be used by industry, government, and academia. The roadmap was developed over a three-year period and coordinated by an international effort of 22 taskforce members from 10 countries and 17 organizations, including universities, research institutes, and sensor companies. In this paper, the current status of MR sensors for non-recording

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Phasor Measurement Unit Test Under Interference Conditions

Cited by 27 publications

References 33 publications

Classification of faults in an IEEE 30 bus transmission system using fully convolutional network

Classification of faults in an IEEE 30 bus transmission system using fully convolutional network

A Synchrophasor Data-Driven Method for Forced Oscillation Localization Under Resonance Conditions

Magnetoresistive Sensor Development Roadmap (Non-Recording Applications)

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