Event detection method for the PMUs synchrophasor data

Sohn, Sang-Wook; Allen, A.; Kulkarni, Saurabh; Grady, W.M.; Santoso, Surya

doi:10.1109/pemwa.2012.6316400

Cited by 20 publications

(10 citation statements)

References 6 publications

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“…Event Localization Flow chart . are the analog input to the PMU installed at Dehgam) [3]. The result correctly predicts the feeder in the direction of which there is a probability of fault.…”

Section: B Phase To Phase Faultmentioning

confidence: 98%

“…The analysis consists of three windows (prior, target and posterior) each of length "W" and are separated by certain number of samples defined as detection range "t" [3]. The windowing technique divides the detection range into three ranges of equal duration, a target detection range (t1), a prior detection range (ending t2 seconds before the target range) and a posterior range (beginning t3 seconds after the target range), as shown in Fig 6. The event detection algorithm analyzes these windows for decision rules to confirm the occurrence of event in the target window.…”

Section: A Event Detection Algorithmmentioning

confidence: 99%

“…Reference [3] describes the use of various statistical methods to identify the possibility of an event. Such event detection methods will provide the information about the range of data on which a detailed analysis needs to be performed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Disturbance analysis tool based on synchrophasor data

Mukhopadhyay¹,

Anumasula²,

Kumar³

et al. 2014

2014 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

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Indian grid is among the largest synchronous grid in the world. With rapid growth in demand and generation and increased complexity in transmission network, the grid operation is becoming challenging day by day. With increased complexity, power system dynamic state visualization has a major role in grid operation. This has been achieved with the introduction of Synchrophasor Measurement Units also known as Phasor Measurement Units (PMUs). With the help of PMU, the grid dynamics now can be monitored at sub-second level at control centre. With the help of the sub-second data and phasor information, the power system fault can be easily diagnosed in real time which will help the operator in reducing the after effects and restoration time. This paper is focused on the development of Disturbance Analysis (DA) tool for analyzing faults in the grid using synchrophasor data and its localization at control centre. This will help operator in immediately accessing the fault and taking corrective measures. Various case studies analyzed using the developed DA Tool are discussed in the paper. This tool has eased the process of analysis and reduced the time for detailed analysis of grid event which has increased the operator confidence level during restoration. This tool has laid the path forward for the development of analysis tools as per the user conveyance to monitor the grid.

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Section: B Phase To Phase Faultmentioning

confidence: 98%

Section: A Event Detection Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Disturbance analysis tool based on synchrophasor data

Mukhopadhyay¹,

Anumasula²,

Kumar³

et al. 2014

2014 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

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“…Whereas, the work presented in [22] uses a generator clustering approach to determine the source of an event based on detecting the largest initial rotor swing. Other works have dealt with screening volumes of data for significant events, applying algorithms based on Fourier transforms and Yule Walker methods [23], [24].…”

Section: The Design Of Pdfamentioning

confidence: 99%

Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

Khan

Ashton

et al. 2015

IEEE Trans. Smart Grid

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Abstract-Phasor Measurement Units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronised measurements. The devices high data reporting rates present major computational challenges, in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents PDFA, a parallel detrended fluctuation analysis approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain, from the aspects of speedup, scalability and accuracy. The speedup of the PDFA in computation is initially analysed through Amdahl's Law, a revision to the law is then proposed, suggesting enhancements to its capability to analyse the performance gain in computation when parallelizing data intensive applications in a cluster computing environment.

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“…Use of short-time Fourier transform (STFT) was applied for analyzing voltage signal event [15], but the performance depends on the size of window sample length. In [16], authors discussed event detection scheme using statistical measures, residual modeling, STFT and slope of phase angle signal. Wavelet analysis is established technique for non-stationary signals [17], however require appropriate sampling rate.…”

Section: Introductionmentioning

confidence: 99%