Neural network-based power system dynamic state estimation using hybrid data from SCADA and phasor measurement units

This paper points out the application of artificial neural network for short-term load forecasting where the projected loads are utilized to define a discrete-time state transition model (i.e., process model). The model is applied to estimate states dynamically and to generate pseudo measurements. Weights of neural network are not treated static and would be carried out under reevaluation alongside the estimation of state vector dynamically. The unscented Kalman filter estimation approach, which requires less approximation of power system, is used in the proposed method. The unscented Kalman filter is implemented through a dual structure due to the interactions of the state vector and the dynamic model of power system. The performance of the proposed method from accuracy prospective is compared with a couple of widely used methods. An optimum solution for wide-area monitoring system would be realized through implementation of more realistic process model along the simplicity of the proposed method and its capability to handle hybrid measurement data from SCADA and phasor measurement units.