Visible to the public State Estimation Error Detection System for Online Dynamic Security Assessment

TitleState Estimation Error Detection System for Online Dynamic Security Assessment
Publication TypeConference Paper
Year of Publication2017
AuthorsTsujii, Y., Kawakita, K. E., Kumagai, M., Kikuchi, A., Watanabe, M.
Conference Name2017 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT)
Keywordscompositionality, DSA analysis, DSA assessment, Dynamic security assessment, dynamical system, electrical power system, generator shedding scheme, Human Behavior, Hybrid power systems, Load flow, load shedding, online dynamic security assessment, online-DSA, phasor measurement, phasor measurement units, Phasor Measurements Units, PMU observation data, power flow value, Power measurement, power system, power system monitoring, power system security, power system stability control, power system state estimation, power system transient stability, pubcrawl, Q measurement, remedial action schemes, resilience, Resiliency, SCADA, SCADA systems, SE error detection system, security, state estimation, state estimation error detection system, supervisory control and data acquisition, transient stability

Online Dynamic Security Assessment (DSA) is a dynamical system widely used for assessing and analyzing an electrical power system. The outcomes of DSA are used in many aspects of the operation of power system, from monitoring the system to determining remedial action schemes (e.g. the amount of generators to be shed at the event of a fault). Measurement from supervisory control and data acquisition (SCADA) and state estimation (SE) results are the inputs for online-DSA, however, the SE error, caused by sudden change in power flow or low convergence rate, could be unnoticed and skew the outcome. Therefore, generator shedding scheme cannot achieve optimum but must have some margin because we don't know how SE error caused by these problems will impact power system stability control. As a method for solving the problem, we developed SE error detection system (EDS), which is enabled by detecting the SE error that will impact power system transient stability. The method is comparing a threshold value and an index calculated by the difference between SE results and PMU observation data, using the distance from the fault point and the power flow value. Using the index, the reliability of the SE results can be verified. As a result, online-DSA can use the SE results while avoiding the bad SE results, assuring the outcome of the DSA assessment and analysis, such as the amount of generator shedding in order to prevent the power system's instability.

Citation Keytsujii_state_2017