Application of Compressive Sampling in Synchrophasor Data Communication in WAMS

Das, Sarasij; Sidhu, T.S.

doi:10.1109/tii.2013.2272088

Cited by 54 publications

(24 citation statements)

References 33 publications

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“…Bandwidth requirement of synchrophasor communication network increases with the number of installed PMUs. Power utilities often find it difficult to arrange large investments to build/upgrade communication networks to meet the increased bandwidth demand [69]. Bandwidth requirements of synchrophasor communication networks also increase with increasing synchrophasor reporting rates.…”

Section: Wide Area Measurement Systems (Wams)mentioning

confidence: 99%

“…The papers [68,69] identify the areas of synchrophasor communications which can be improved by the application of compressive sampling (CS). The sparsity of synchrophasor data has been discussed in [68,69] to justify the application of CS.…”

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confidence: 99%

“…In [69], it is shown that the CS can be effective in reducing the bandwidth requirements of WAMS networks. Synchrophasors corresponding to various power system scenarios are considered.…”

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confidence: 99%

“…Results show that the CS can reconstruct synchrophasors with sufficient accuracies during various power system scenarios. In [69], CS sampling is designed to minimize communication delays while sending synchrophasors. It is proposed that PMUs send synchrophasors as soon as they are computed (not in batch).…”

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confidence: 99%

“…Missing and bad data often pose challenges to the WAMS applications. In [69], it is shown that missing or bad synchrophasors can be successfully estimated using CS. It is demonstrated that CS performs satisfactorily in presence of noise.…”

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confidence: 99%

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Application of Compressive Sampling in Computer Based Monitoring of Power Systems

Das

Sidhu

2014

Advances in Computer Engineering

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Shannon's Nyquist theorem has always dictated the conventional signal acquisition policies. Power system is not an exception to this. As per this theory, the sampling rate must be at least twice the maximum frequency present in the signal. Recently, compressive sampling (CS) theory has shown that the signals can be reconstructed from samples obtained at sub-Nyquist rate. Signal reconstruction in this theory is exact for "sparse signals" and is near exact for compressible signals provided certain conditions are satisfied. CS theory has already been applied in communication, medical imaging, MRI, radar imaging, remote sensing, computational biology, machine learning, geophysical data analysis, and so forth. CS is comparatively new in the area of computer based power system monitoring. In this paper, subareas of computer based power system monitoring where compressive sampling theory has been applied are reviewed. At first, an overview of CS is presented and then the relevant literature specific to power systems is discussed.

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Section: Wide Area Measurement Systems (Wams)mentioning

confidence: 99%

mentioning

confidence: 99%

“…In [69], it is shown that the CS can be effective in reducing the bandwidth requirements of WAMS networks. Synchrophasors corresponding to various power system scenarios are considered.…”

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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