Partition-composition method for online detection of interconnected power system transient stability

A new partition-composition method for online detection of transient stability of interconnected power system with the data from wide area measurement system (WAMS) is proposed. It consists of two parts: partition and composition. In the partition step, interconnected grids are divided into several local areas according to their geographical locations or ownership. Each area is modelled as an adjoint power system (APS) to determine its own synchronism. In the composition step, transient stability of entire grid is evaluated by the APS model of entire system composed with several key parameters of local areas, which can be directly derived from local WAMS. The proposed method only uses WAMS data, hence it is independent from the offline data, such as network topology, device model and system parameters. Another outstanding feature of the proposed method is that only a few key parameters are required to be transferred from the local control centres to the global control centre. These features are helpful for the interconnected grid with different owners since exhaustive data are unnecessary for the global control centre. Case studies on the 10-generator New England system and one of 2007 operation cases of North China Power Grid validate its correctness and effectiveness.

[1]  Rui Zhang,et al.  Real-time transient stability assessment model using extreme learning machine , 2011 .

[2]  Binod Shrestha,et al.  Out-of-step protection using state plane trajectories analysis , 2011, 2014 IEEE PES General Meeting | Conference & Exposition.

[3]  U. D. Annakkage,et al.  Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements , 2011, IEEE Transactions on Power Systems.

[4]  P. Kundur,et al.  Dynamic reduction of large power systems for stability studies , 1997 .

[5]  Mania Pavella,et al.  Generalized one-machine equivalents in transient stability studies , 1998 .

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

[7]  T. S. Chung,et al.  Transient stability limit conditions analysis using a corrected transient energy function approach , 2000 .

[8]  K.R. Padiyar,et al.  Online detection of loss of synchronism using energy function criterion , 2006, IEEE Transactions on Power Delivery.

[9]  Soheil Ranjbar,et al.  Transient Instability Prediction Using Decision Tree Technique , 2013, IEEE Transactions on Power Systems.

[10]  Felix F. Wu,et al.  Foundations of the potential energy boundary surface method for power system transient stability analysis , 1988 .

[11]  Sumit Paudyal,et al.  Application of equal area criterion conditions in the time domain for out-of-step protection , 2010, IEEE PES General Meeting.

[12]  Xiuling Zhang,et al.  "Beyond-thermal-equilibrium" conversion of methane to acetylene and hydrogen under pulsed corona discharge , 2002 .

[13]  A.A. Girgis,et al.  New method for generators' angles and angular velocities prediction for transient stability assessment of multimachine power systems using recurrent artificial neural network , 2004, IEEE Transactions on Power Systems.

[14]  H. Jia,et al.  Criterion to evaluate power system online transient stability based on adjoint system energy function , 2015 .

[15]  Ping Ju,et al.  Dynamic equivalents of power systems with online measurements. Part 1: Theory , 2004 .

[16]  S. Q. Yuan,et al.  Transient stability assessment using projection formulations , 2009 .