Impact of False Data Detection on Cloud Hosted Linear State Estimator Performance

Vinaya Chakati, Madhurima Pore, Ayan Banerjee, Anamitra Pal, Sandeep Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Linear state estimation (LSE) is used to compute the complex voltages of a power system using measurements obtained only from phasor measurement units (PMUs). With the continued addition of PMUs into the grid, classical LSE solvers would have to handle large sets of high-speed data. Furthermore, security threats in the form of false data injection (FDI) attacks must also be considered in the design, which will considerably add to the computational overhead of LSE solvers. Although installing additional computation and communication hardware is a possible solution, such a solution would incur substantial infrastructure and operation costs. In this paper, we explore the design of a cost-effective and scalable cloud hosted LSE (CLSE) solver that also has false data detection (FDD). The proposed CLSE-FDD application exploits GPU parallel processing capabilities for mitigating the performance overhead of FDD to match the operation speed of classical LSE solvers. Results indicate that the GPU based CLSE-FDD application can easily scale in excess of 1,500 PMU installations.

Original languageEnglish (US)
Title of host publication2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PublisherIEEE Computer Society
Volume2018-August
ISBN (Electronic)9781538677032
DOIs
StatePublished - Dec 21 2018
Event2018 IEEE Power and Energy Society General Meeting, PESGM 2018 - Portland, United States
Duration: Aug 5 2018Aug 10 2018

Other

Other2018 IEEE Power and Energy Society General Meeting, PESGM 2018
CountryUnited States
CityPortland
Period8/5/188/10/18

Keywords

  • Cloud Computing
  • False Data Detection
  • GPU
  • Linear State Estimation
  • Phasor Measurement Unit (PMU)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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  • Cite this

    Chakati, V., Pore, M., Banerjee, A., Pal, A., & Gupta, S. (2018). Impact of False Data Detection on Cloud Hosted Linear State Estimator Performance. In 2018 IEEE Power and Energy Society General Meeting, PESGM 2018 (Vol. 2018-August). [8586671] IEEE Computer Society. https://doi.org/10.1109/PESGM.2018.8586671