The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends

Tcheou, Michel P.; Lovisolo, Lisandro; Ribeiro, Moisés V.; Silva, Eduardo A. B. da; Rodrigues, Marcos Vinícius; Romano, João Marcos Travassos; Diniz, Paulo S. R.

doi:10.1109/tsg.2013.2293957

Cited by 128 publications

(63 citation statements)

References 88 publications

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“…And it becomes essential to compress this volume so that the data sets can be transmitted and stored promptly and efficiently. Hence, PQ data compression calls more concerns than ever before [4].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Data Compression Based on Iterative PCA Algorithm in Smart Distribution Systems

Zhang¹,

Zhan²,

He³

2017

SGRE

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To reduce the stress of data transmission and storage for power quality (PQ) in smart distribution systems and help PQ analysis, a multichannel data compression based on iterative PCA (principal component analysis) algorithm is introduced. The proposed method uses PCA to reduce the redundancy of data to achieve the purpose of compressing data. In order to improve the calculating speed, an iterative method is proposed to compute the principal components of the covariance matrix. The correctness and feasibility of the proposed method are verified by field PQ data tests. Compared with discrete wavelet transform (DWT) method, the proposed method has good performance on compression ratio and reconstruction accuracy.

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Section: Introductionmentioning

confidence: 99%

Power Quality Data Compression Based on Iterative PCA Algorithm in Smart Distribution Systems

Zhang¹,

Zhan²,

He³

2017

SGRE

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“…Such measurement systems output large amounts of data and signal compression must be used for efficient storage and transmission. The "big data" problem in the smart grid is now being recognized in the literature [5][6][7][8][9][10][11][12][13]. The extensive review of electric signal waveform compression was presented in [6].…”

Section: Introductionmentioning

confidence: 99%

“…The extensive review of electric signal waveform compression was presented in [6]. Most compression algorithms overviewed in [6] operate in lossy mode, i.e. some information is lost after the encoding and decoding process.…”

Section: Introductionmentioning

confidence: 99%

Lossless predictive coding of electric signal waveforms

Duda¹

2017

Turk J Elec Eng & Comp Sci

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This paper describes a new predictive coding algorithm designed for lossless compression of electric signal waveforms. Prediction coefficients are obtained from the sinusoidal signal autoregressive model. Predictors for a single sinusoid and the sum of two and three sinusoids are considered. Integer-to-integer coding is obtained by the proper (i.e. reversible) quantization of computations. The proposed coding algorithm shows better performance than the commonly used 2nd order differences coding and linear prediction coding that is optimal in the least squares sense.

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“…Special attention was given to compression techniques for electric signals [1]. The need for introduction of powerful compression techniques for smart grid applications, and some of the most important research challenges were addressed.…”

Section: Introductionmentioning

confidence: 99%

“…The need for introduction of powerful compression techniques for smart grid applications, and some of the most important research challenges were addressed. It was emphasized that signal compression is crucial to come up with smarter grids for smart sensing, monitoring, and protection in the next generation of electric systems [1].…”

Section: Introductionmentioning

confidence: 99%

Smart Compression for Telemedicine

Sharifahmadian¹,

Latifi²

2014

IJCA

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In many applications in which a huge amount of data is transmitted, and the data is processed in real time, data compression is crucial. Here, a smart compression method based on the wavelet subbands arranging technique is proposed for telemedicine. The proposed method can effectively reduce the large amount of transmitted data, and provide real-time analysis. Moreover, if available bandwidth drops, the proposed method is capable of tuning the rate of compressed data, and different input channels are simultaneously compressed with different rates, as well as the quality of each compressed data is preserved. The proposed method is tested on selected records from the MIT-BIH arrhythmia database. Based on results, it is concluded that the proposed approach is an appropriate choice to intelligently and simultaneously compress large amount of multichannel data, is able to tune compressed data rate to preserve the required quality of output, and to use the bit capacity of every input channel efficiently.

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The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends

Cited by 128 publications

References 88 publications

Power Quality Data Compression Based on Iterative PCA Algorithm in Smart Distribution Systems

Power Quality Data Compression Based on Iterative PCA Algorithm in Smart Distribution Systems

Lossless predictive coding of electric signal waveforms

Smart Compression for Telemedicine

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