TY - GEN
T1 - Impact of False Data Detection on Cloud Hosted Linear State Estimator Performance
AU - Chakati, Vinaya
AU - Pore, Madhurima
AU - Banerjee, Ayan
AU - Pal, Anamitra
AU - Gupta, Sandeep
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/21
Y1 - 2018/12/21
N2 - 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.
AB - 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.
KW - Cloud Computing
KW - False Data Detection
KW - GPU
KW - Linear State Estimation
KW - Phasor Measurement Unit (PMU)
UR - http://www.scopus.com/inward/record.url?scp=85060816897&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060816897&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2018.8586671
DO - 10.1109/PESGM.2018.8586671
M3 - Conference contribution
AN - SCOPUS:85060816897
T3 - IEEE Power and Energy Society General Meeting
BT - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PB - IEEE Computer Society
T2 - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Y2 - 5 August 2018 through 10 August 2018
ER -