Optimal data compression techniques for Smart Grid and power quality trend data

Kraus, Jan; Stepan, Pavel; Kukačka, Leoš

doi:10.1109/ichqp.2012.6381185

Cited by 24 publications

(13 citation statements)

References 6 publications

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“…In order to deal with this large amount of data and to manage the electrical signals, two main categories of mathematical tools, based on compression techniques, have been used: lossless techniques [7][8][9] and lossy algorithms [3,[10][11][12]. Reference [6] reports a comparison of these tools In particular, we firstly introduce the WT used to characterize signals in the time-frequency domain, pointing out the non-stationary behaviors.…”

Section: Introductionmentioning

confidence: 99%

Fourier, Wavelet, and Hilbert-Huang Transforms for Studying Electrical Users in the Time and Frequency Domain

2017

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Abstract:The analysis of electrical signals is a pressing requirement for the optimal design of power distribution. In this context, this paper illustrates how to use a variety of numerical tools, such as the Fourier, wavelet, and Hilbert-Huang transforms, to obtain information relating to the active and reactive power absorbed by different types of users. In particular, the Fourier spectrum gives the most important frequency components of the electrical signals, and the wavelet analysis highlights the non-stationarity of those frequency contributions, whereas the Hilbert-Huang transform, by means of the Empirical Mode Decomposition, provides a more complete spectrum of frequencies.

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Section: Introductionmentioning

confidence: 99%

Fourier, Wavelet, and Hilbert-Huang Transforms for Studying Electrical Users in the Time and Frequency Domain

2017

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“…The compression of phasor measurement units (PMUs) data is the field most closely related to smart meter readings compression. In [6], different data compression techniques for PMU readings are discussed and evaluated. Ning et al [7] proposed a wavelet-based compression technique for the readings of PMUs.…”

Section: Related Workmentioning

confidence: 99%

“…2, which exploits the load profile data characteristics described in Section III. Our algorithm takes a list of values from a load profile (1) as input and outputs a compressed binary representation of it (6). In the following, the five processing steps (A-E) are described.…”

Section: Proposed Compression Approachmentioning

confidence: 99%

Resumable Load Data Compression in Smart Grids

Unterweger

Engel

2015

IEEE Trans. Smart Grid

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We propose a compression approach for load profile data, which addresses practical requirements of smart metering. By providing linear time complexity with respect to the input data size, our compression approach is suitable for low-complexity encoding and decoding for storage and transmission of load profile data in smart grids. Furthermore, it allows for resumability with very low overhead on error-prone transmission lines, which is an important feature not available for standard time series compression schemes. In terms of compression efficiency, our approach outperforms transmission encodings that are currently used for electricity metering by an order of magnitude.

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“…The former includes image compression techniques, and the latter gathers discrete transformations and parametric coding and mixed solutions. Lossless compression algorithms (LCA) of archives in the smart grid and PQ data series are focused on solving the aggregation resulting from a continuous monitoring [21]. Among some works, we remark about the use of 2D representations for compact energy coefficients and avoiding data redundancy in the work [22].…”

Section: Pq Data Organization Tendencies: the Need For Compressionmentioning

confidence: 99%

Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid

et al. 2018

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The increasing development of the smart grid demands reliable monitoring of the power quality at different levels, introducing more and more measurement points. In this framework, the advanced metering infrastructure must deal with this large amount of data, storage capabilities, improving visualization, and introducing customer-oriented interfaces. This work proposes a method that optimizes the smart grid data, monitoring the real voltage supplied based on higher order statistics. The method proposes monitoring the network from a scalable point of view and offers a two-fold perspective based on the duality utility-prosumer as a function of the measurement time. A global PQ index and 2D graphs are introduced in order to compress the time domain information and quantify the deviations of the waveform shape by means of three parameters. Time-scalability allows two extra features: long-term supply reliability and power quality in the short term. As a case study, the work illustrates a real-life monitoring in a building connection point, offering 2D diagrams, which show time and space compression capabilities, as well.Keywords: signal waveform compression; higher-order statistics (HOS); power quality (PQ); computational solutions for advanced metering infrastructure (AMI); smart grid (SG) applications

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Optimal data compression techniques for Smart Grid and power quality trend data

Cited by 24 publications

References 6 publications

Fourier, Wavelet, and Hilbert-Huang Transforms for Studying Electrical Users in the Time and Frequency Domain

Fourier, Wavelet, and Hilbert-Huang Transforms for Studying Electrical Users in the Time and Frequency Domain

Resumable Load Data Compression in Smart Grids

Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid

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