Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

Liu, Shengyuan; Zhao, Yuxuan; Lin, Zhenzhi; Liu, Yilu; Ding, Yi; Yang, Li; Yi, Shimin

doi:10.1109/tsg.2019.2941565

Title: Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

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Abstract

With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. Overall, the results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhancemore »« less

Authors:

^[1];

^[2]; Yi, Shimin ^[3]

Zhejiang Univ., Hangzhou (China)
Univ. of Tennessee, Knoxville, TN (United States)
Guangdong Power Grid Co., Ltd., Guangzhou (China)

Publication Date:: Sun Mar 01 00:00:00 EST 2020

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1661203

Alternate Identifier(s):: OSTI ID: 1665971

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Smart Grid

Additional Journal Information:: Journal Volume: 11; Journal Issue: 2; Journal ID: ISSN 1949-3053

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Phasor measurement units; principal component analysis; event detection; power system dynamics; heuristic algorithms; wide area measurements; similarity search method; local outlier factor (LOF)

Citation Formats


                    Liu, Shengyuan, Zhao, Yuxuan, Lin, Zhenzhi, Liu, Yilu, Ding, Yi, Yang, Li, and Yi, Shimin. Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor.  United States: N. p., 2020. 
Web.  doi:10.1109/tsg.2019.2941565.

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                    Liu, Shengyuan, Zhao, Yuxuan, Lin, Zhenzhi, Liu, Yilu, Ding, Yi, Yang, Li, & Yi, Shimin. Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor.  United States.  https://doi.org/10.1109/tsg.2019.2941565

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                    Liu, Shengyuan, Zhao, Yuxuan, Lin, Zhenzhi, Liu, Yilu, Ding, Yi, Yang, Li, and Yi, Shimin. Sun .  
"Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor".  United States.  https://doi.org/10.1109/tsg.2019.2941565.  https://www.osti.gov/servlets/purl/1661203.

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@article{osti_1661203,

  title        = {Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor},

  author       = {Liu, Shengyuan and Zhao, Yuxuan and Lin, Zhenzhi and Liu, Yilu and Ding, Yi and Yang, Li and Yi, Shimin},

  abstractNote = {With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. Overall, the results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.},

  doi          = {10.1109/tsg.2019.2941565},

  journal      = {IEEE Transactions on Smart Grid},

  number       = 2,

  volume       = 11,

  place        = {United States},

  year         = {Sun Mar 01 00:00:00 EST 2020},

  month        = {Sun Mar 01 00:00:00 EST 2020}

}

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