Title :
Dynamic state estimation of power system based on grey system theory and strong tracking filter
Author :
Jianping Tang ; Darong Huang ; Ling Zhao
Author_Institution :
Inst. of Inf. Sci. & Eng., Chongqing Jiaotong Univ., Chongqing, China
Abstract :
Currently, the dynamic state estimation of power systems constructing based extended Kalman Filter(EKF) has some shortages such as slow convergence and low robustness etc.. For that, a new dynamic state estimation method was presented based on Grey System Theory(GST) and Strong Tracking Filter(STF). Firstly, the correlation degree analysis was conducted and GM(1,1) model was established by using the sate historical data of power systems. And then the forecast values of state vector were computed in forecasting model. Secondly, by measuring innovation information of filtering data, we had constructed a filtering model of forecasting values based STF. Finally, some simulation examples showed that the theoretical analysis and algorithm are effectively and practicably.
Keywords :
Kalman filters; energy measurement; filtering theory; forecasting theory; grey systems; nonlinear filters; power system state estimation; vectors; EKF; GM(1,1) model; GST; STF; correlation degree analysis; dynamic state estimation method; energy management system; extended Kalman filter; filtering data; forecast values; forecasting model; grey system theory; innovation information measurement; power systems; sate historical data; state vector; strong tracking filter; Correlation; Filtering theory; Information filtering; Power system dynamics; Predictive models; State estimation; Vectors; dynamic state estimation; grey system theory; power system; strong tracking filter;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561696
