Perturbed Trajectory Prediction Method based on Simplified Power System Model

With the interconnection of power grid, the post-disturbance system is easier to suffer power-angle swing and system oscillation. Moreover, the impact of load characteristics on the system dynamic behavior becomes more obvious. Therefore a new perturbed trajectory prediction method considering load model is presented in this paper. A static load model with voltage characteristics instead of pure impedance is introduced into the system models. The admittance matrix including load nodes and a coefficient matrix of load proportion are figured out based on the real-time data from WAMS (wide-area measurement system). Using the sensitivity equation of power flow, the linear relationship between the micro variation of electromagnetic power and that of rotor angle is deduced, and then a new perturbed trajectory prediction algorithm is proposed. Numerical simulation results on the IEEE-9 bus system show that the proposed method works well

[1]  H.C.S. Rughooputh,et al.  Real-time transient stability prediction using neural tree networks , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[2]  P. Kundur,et al.  Power system stability and control , 1994 .

[3]  Zhang Bao Real time transient stability prediction for multi machine power system based on phasor measurement units , 2000 .

[4]  Mu-Chun Su,et al.  Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements , 1999 .

[5]  James S. Thorp,et al.  Decision trees for real-time transient stability prediction , 1994 .

[6]  Li Yi-qun,et al.  The study on real-time transient stability emergency control in power system , 2002, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373).

[7]  Chih-Wen Liu,et al.  New methods for computing power system dynamic response for real-time transient stability prediction , 2000 .

[8]  Liu Yu APPLICATION OF PMU AND FUZZY RADIAL BASIS FUNCTION NETWORK TO POWER SYSTEM TRANSIENT STABILITY PREDICTION , 2000 .

[9]  Yutian Liu,et al.  Real-time transient stability prediction using incremental learning algorithm , 2004, IEEE Power Engineering Society General Meeting, 2004..

[10]  Chen Chen Transient Stability Prediction Using Time-Series Based on GPS Synchronized Measurement , 2001 .

[11]  Arun G. Phadke,et al.  Synchronized phasor measurements-a historical overview , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[12]  Su Jian Power systems transient stability prediction by using fuzzy neural network based on GPS synchronized measurements , 2001 .