Event Detection from PMU Generated Big Data using R Programming

Vishwajit Roy; Subrina Sultana Noureen; Stephen B. Bayne; Argenis Bilbao; Michael Giesselmann

doi:10.1109/SusTech.2018.8671342

Event Detection from PMU Generated Big Data using R Programming

Vishwajit Roy, Subrina Sultana Noureen, Stephen B. Bayne, Argenis Bilbao, Michael Giesselmann

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Recent advancement in Power System Analysis shows that implementation of PMU (Phasor Measurement Unit) in Smart Grid playing a significant role over SCADA. The main reasoning for that is more sampling data than traditional SCADA system. Every PMU data like voltage, current and Phase angle gives more samples in every second which is helpful for event detection. The enormous data send by each PMU in every second energies the big data issue. To find out and predict the transient situation and even small disturbances or anomalies from big data analysis within the specified short period of time is a challenge for near future. Because introduction of new smart electrical devices will boost up the big data issue. Processing of big data for post disturbance analysis is also an important task. This paper gives a scenario of PMU measurements received to PDC (Phasor Data Concentrator) from PMUs placed in distinct locations and detection of transient events for post disturbance analysis. In this analysis, the disturbances are evaluated with the R programming analysis and compare findings of chronological data from separate locations and also shows the relation between disturbances in a grid. For this analysis, the impacts of frequency and voltage data are also considered.

Original language	English
Title of host publication	2018 IEEE Conference on Technologies for Sustainability, SusTech 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538677919
DOIs	https://doi.org/10.1109/SusTech.2018.8671342
State	Published - Mar 18 2019
Event	2018 IEEE Conference on Technologies for Sustainability, SusTech 2018 - Long Beach, United States Duration: Nov 11 2018 → Nov 13 2018

Publication series

Name	2018 IEEE Conference on Technologies for Sustainability, SusTech 2018

Conference

Conference	2018 IEEE Conference on Technologies for Sustainability, SusTech 2018
Country/Territory	United States
City	Long Beach
Period	11/11/18 → 11/13/18

Keywords

Big Data
Event detection
PDC
PMU
Smart grid
Wide Area Monitoring System (WAMS)

Access to Document

10.1109/SusTech.2018.8671342

Cite this

Roy, V., Noureen, S. S., Bayne, S. B., Bilbao, A., & Giesselmann, M. (2019). Event Detection from PMU Generated Big Data using R Programming. In 2018 IEEE Conference on Technologies for Sustainability, SusTech 2018 [8671342] (2018 IEEE Conference on Technologies for Sustainability, SusTech 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SusTech.2018.8671342

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title = "Event Detection from PMU Generated Big Data using R Programming",

abstract = "Recent advancement in Power System Analysis shows that implementation of PMU (Phasor Measurement Unit) in Smart Grid playing a significant role over SCADA. The main reasoning for that is more sampling data than traditional SCADA system. Every PMU data like voltage, current and Phase angle gives more samples in every second which is helpful for event detection. The enormous data send by each PMU in every second energies the big data issue. To find out and predict the transient situation and even small disturbances or anomalies from big data analysis within the specified short period of time is a challenge for near future. Because introduction of new smart electrical devices will boost up the big data issue. Processing of big data for post disturbance analysis is also an important task. This paper gives a scenario of PMU measurements received to PDC (Phasor Data Concentrator) from PMUs placed in distinct locations and detection of transient events for post disturbance analysis. In this analysis, the disturbances are evaluated with the R programming analysis and compare findings of chronological data from separate locations and also shows the relation between disturbances in a grid. For this analysis, the impacts of frequency and voltage data are also considered.",

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author = "Vishwajit Roy and Noureen, {Subrina Sultana} and Bayne, {Stephen B.} and Argenis Bilbao and Michael Giesselmann",

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year = "2019",

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doi = "10.1109/SusTech.2018.8671342",

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Roy, V, Noureen, SS, Bayne, SB, Bilbao, A & Giesselmann, M 2019, Event Detection from PMU Generated Big Data using R Programming. in 2018 IEEE Conference on Technologies for Sustainability, SusTech 2018., 8671342, 2018 IEEE Conference on Technologies for Sustainability, SusTech 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Conference on Technologies for Sustainability, SusTech 2018, Long Beach, United States, 11/11/18. https://doi.org/10.1109/SusTech.2018.8671342

Event Detection from PMU Generated Big Data using R Programming. / Roy, Vishwajit; Noureen, Subrina Sultana; Bayne, Stephen B. et al.

2018 IEEE Conference on Technologies for Sustainability, SusTech 2018. Institute of Electrical and Electronics Engineers Inc., 2019. 8671342 (2018 IEEE Conference on Technologies for Sustainability, SusTech 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Noureen, Subrina Sultana

AU - Bayne, Stephen B.

AU - Bilbao, Argenis

AU - Giesselmann, Michael

PY - 2019/3/18

Y1 - 2019/3/18

N2 - Recent advancement in Power System Analysis shows that implementation of PMU (Phasor Measurement Unit) in Smart Grid playing a significant role over SCADA. The main reasoning for that is more sampling data than traditional SCADA system. Every PMU data like voltage, current and Phase angle gives more samples in every second which is helpful for event detection. The enormous data send by each PMU in every second energies the big data issue. To find out and predict the transient situation and even small disturbances or anomalies from big data analysis within the specified short period of time is a challenge for near future. Because introduction of new smart electrical devices will boost up the big data issue. Processing of big data for post disturbance analysis is also an important task. This paper gives a scenario of PMU measurements received to PDC (Phasor Data Concentrator) from PMUs placed in distinct locations and detection of transient events for post disturbance analysis. In this analysis, the disturbances are evaluated with the R programming analysis and compare findings of chronological data from separate locations and also shows the relation between disturbances in a grid. For this analysis, the impacts of frequency and voltage data are also considered.

AB - Recent advancement in Power System Analysis shows that implementation of PMU (Phasor Measurement Unit) in Smart Grid playing a significant role over SCADA. The main reasoning for that is more sampling data than traditional SCADA system. Every PMU data like voltage, current and Phase angle gives more samples in every second which is helpful for event detection. The enormous data send by each PMU in every second energies the big data issue. To find out and predict the transient situation and even small disturbances or anomalies from big data analysis within the specified short period of time is a challenge for near future. Because introduction of new smart electrical devices will boost up the big data issue. Processing of big data for post disturbance analysis is also an important task. This paper gives a scenario of PMU measurements received to PDC (Phasor Data Concentrator) from PMUs placed in distinct locations and detection of transient events for post disturbance analysis. In this analysis, the disturbances are evaluated with the R programming analysis and compare findings of chronological data from separate locations and also shows the relation between disturbances in a grid. For this analysis, the impacts of frequency and voltage data are also considered.

KW - Big Data

KW - Event detection

KW - PDC

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KW - Smart grid

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 11 November 2018 through 13 November 2018

ER -

Event Detection from PMU Generated Big Data using R Programming

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