Development of the Decoupled Discreet-Time Jacobian Eigenvalue Approximation for Situational Awareness Utilizing Open PDC  

作　　者：Sean D. Kantra Elham B. Makram Sean D. Kantra;Elham B. Makram(Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, USA)

机构地区：[1]Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, USA

出　　处：《Journal of Power and Energy Engineering》2016年第9期21-35,共15页电力能源（英文）

摘　　要：With the increased number of PMUs in the power grid, effective high speed, realtime methods to ascertain relevant data for situational awareness are needed. Several techniques have used data from PMUs in conjunction with state estimation to assess system stability and event detection. However, these techniques require system topology and a large computational time. This paper presents a novel approach that uses real-time PMU data streams without the need of system connectivity or additional state estimation. The new development is based on the approximation of the eigenvalues related to the decoupled discreet-time power flow Jacobian matrix using direct openPDC data in real-time. Results are compared with other methods, such as Prony’s method, which can be too slow to handle big data. The newly developed Discreet-Time Jacobian Eigenvalue Approximation (DDJEA) method not only proves its accuracy, but also shows its effectiveness with minimal computational time: an essential element when considering situational awareness.With the increased number of PMUs in the power grid, effective high speed, realtime methods to ascertain relevant data for situational awareness are needed. Several techniques have used data from PMUs in conjunction with state estimation to assess system stability and event detection. However, these techniques require system topology and a large computational time. This paper presents a novel approach that uses real-time PMU data streams without the need of system connectivity or additional state estimation. The new development is based on the approximation of the eigenvalues related to the decoupled discreet-time power flow Jacobian matrix using direct openPDC data in real-time. Results are compared with other methods, such as Prony’s method, which can be too slow to handle big data. The newly developed Discreet-Time Jacobian Eigenvalue Approximation (DDJEA) method not only proves its accuracy, but also shows its effectiveness with minimal computational time: an essential element when considering situational awareness.

关 键 词：SYNCHROPHASOR PMU Open PDC Power Flow Jacobian Decoupled Discreet-Time Jacobian Approximation Singular Value Decomposition (SVD) Prony Analysis 

分 类 号：O17[理学—数学]