Algorithm for screening PMU data for power system events

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

doi:10.1109/isgteurope.2012.6465867

Cited by 15 publications

(9 citation statements)

References 12 publications

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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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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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“…Due to the expensive cost of procuring real hardware,, a virtual synchrophasor monitoring network (PMU and PDC) using LabVIEW has been developed in [14], to be used as a teaching tool. A synchrophasor network was designed and used to monitor the PMU data for both on-line and off-line data analysis in [15]. The authors in [16] uses multiple data classification algorithms (k-means and Naïve Bayes) for classification and fault detections in synchrophasor data.…”

Section: Literature Reviewmentioning

confidence: 99%

Using phasor data for visualization and data mining in smart-grid applications

Mukherjee

Vallakati

Lachenaud

et al. 2015

2015 IEEE First International Conference on DC Microgrids (ICDCM)

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This paper presents a density based clustering (DBSCAN) technique to visualize and analyze the smart-grid data. The technique will aid in detecting bad-data, various fault types, deviation on frequency, voltage or current values for better situational awareness. Synchrophasors (or a PMU) is a sensor placed on a transmission line that tracks voltage, current, phase and frequency of the line. To improve situational awareness of the smart grid monitoring in real-time, the utility must monitor the phasor data measurement delivered by the sensors. Time-stamped synchronized measurements offer tremendous benefit for pre and post-event analysis. The paper uses data from openPDC framework to aid system operators in carrying various predictive analytics, decisions.

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“…For a given set of voltage data, a sliding window of data is examined and statistical characteristics of that window are recorded. In examining the evolution of these parameters over time, abnormal or sudden changes in value can indicate the occurrence of an event [16], [17]. One example parameter used was the standard deviation, which is primarily used to detect rapid increases/decreases in voltage magnitude or frequency.…”

Section: Event Similaritymentioning

confidence: 99%

Event analysis of distribution system voltage data

Phillips

Overbye

2015

2015 IEEE Power and Energy Conference at Illinois (PECI)

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Discussion of the future of the Smart Grid brings with it issues regarding the current level of distribution system visibility. Phasor Measurement Units (PMU) have been proliferating throughout the electric power grid in order to support future distribution system analysis. One type of PMU is the Frequency Disturbance Recorder (FDR); it records voltage magnitude, frequency, and phase angle at the 120 V outlet level. Previous work has introduced ways in which distribution system events can be read from this voltage data, but this paper focuses on defining similarity measures for comparing detected events. Discrete Signal Processing (DSP) techniques such as correlation and convolution can be used to determine how similar events are, even in the presence of a time delay. Euclidean distance and mean-squared error are used as similarity measurements to help confirm the correctness of the time-delay estimation. Beyond detecting positively and negatively correlated events, the similarity measures support the correlation results by determining that some events aren't correlated at all.

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Algorithm for screening PMU data for power system events

Cited by 15 publications

References 12 publications

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

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

Using phasor data for visualization and data mining in smart-grid applications

Event analysis of distribution system voltage data

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