STUDY OF ESTIMATING DYNAMIC STATE JACOBIAN MATRIX AND DYNAMIC SYSTEM STATE MATRIX BASED ON PMU

Abstract

Large and extensive power system is dynamic in nature and changes occur very rapidly. Stability condition of power system need to be assessed continuously to prevent collapse of power grid. Stability of power system can be estimated with dynamic state Jacobian matrix and dynamic system state matrix. Conventionally, these matrices are estimated based on State Estimation and by assuming initial network parameter values. Also, there are possibilities of corrupted information of the assumed network model which may results in erroneous estimation of the Jacobian matrices and the state matrix. In this work, a Synchrophasor measurement-based method has been studied to calculate the dynamic state Jacobian matrix and Dynamic system state matrix in varying load conditions. The advantages of these synchronized measurement based methods is high speed and completely model free. Therefore, there is no need of system model information such as network topology and parameters for calculation of Dynamic state Jacobian matrix and system state matrix. A case study on WSCC 9-bus, 3-machine system has been performed in Digsilent Power factory Simulator Software for simulation of ambient conditions. For this purpose, we have developed a low cost laboratory scale setup of PMU architecture for development of PMU applications on MATLAB platform, based on an open source software developed by Grid Protection Alliance, Delft University of technology and Digsilent Power Factory Simulator software. The resultant matrices obtained using proposed method and model based method has been compared and error is estimated. The critical Eigen values are estimated of the System state matrix to show a good measure of proximity of instability by observing the right most points on the eigen value plots.