Publication

Multiple Line Outage Detection in Power Systems by Sparse Recovery Using Transient Data

Ping Hu, Feng Liu, Li Ding
2021
Article
Résumé

Fast and accurate transmission line outage detection can help the central control unit to respond rapidly to better maintain the security and reliability of power systems. It is especially critical in the situation of multiple line outages which is more likely to trigger cascading failures. In this article, we investigate the problem of multiple line outage detection through sparse representation based on the transient dynamics captured by Phasor Measurement Units (PMUs). An accumulation-based method is proposed to reduce the noise interference. By introducing an adaptive threshold selection rule, a modified sparse signal recovery algorithm is proposed to improve the performance of multiple line outage detection. Furthermore, an event-triggered mechanism is presented to reduce the computation burden. Finally, numerical experiments based on the IEEE Standard Test Bus System are conducted to illustrate the effectiveness of our method.

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Concepts associés (25)
Phasor measurement unit
A phasor measurement unit (PMU) is a device used to estimate the magnitude and phase angle of an electrical phasor quantity (such as voltage or current) in the electricity grid using a common time source for synchronization. Time synchronization is usually provided by GPS or IEEE 1588 Precision Time Protocol, which allows synchronized real-time measurements of multiple remote points on the grid. PMUs are capable of capturing samples from a waveform in quick succession and reconstructing the phasor quantity, made up of an angle measurement and a magnitude measurement.
Sparse dictionary learning
Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims at finding a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms and they compose a dictionary. Atoms in the dictionary are not required to be orthogonal, and they may be an over-complete spanning set. This problem setup also allows the dimensionality of the signals being represented to be higher than the one of the signals being observed.
Acquisition comprimée
L'acquisition comprimée (en anglais compressed sensing) est une technique permettant de trouver la solution la plus parcimonieuse d'un système linéaire sous-déterminé. Elle englobe non seulement les moyens pour trouver cette solution mais aussi les systèmes linéaires qui sont admissibles. En anglais, elle porte le nom de Compressive sensing, Compressed Sampling ou Sparse Sampling.
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Publications associées (32)

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The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...
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Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

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Looking beyond Pixels

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Sparse recovery is a powerful tool that plays a central role in many applications, including source estimation in radio astronomy, direction of arrival estimation in acoustics or radar, super-resolution microscopy, and X-ray crystallography. Conventional a ...
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MOOCs associés (6)
Digital Signal Processing I
Basic signal processing concepts, Fourier analysis and filters. This module can be used as a starting point or a basic refresher in elementary DSP
Digital Signal Processing II
Adaptive signal processing, A/D and D/A. This module provides the basic tools for adaptive filtering and a solid mathematical framework for sampling and quantization
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