Fusion of Multirate Measurements for Nonlinear Dynamic State Estimation of the Power Systems

With the increasing availability of sensors, power system dynamic state estimation (PSDSE) is going to play a critical role in the reliable and efficient operation of power systems. The real-time measurements in today’s power grid are obtained through various types of sensors having different sampling rates, e.g., the traditional SCADA systems with low sampling rates (generally 0.5–2 samples per second), and different groups of phasor measurement units having high sampling rates (usually 30–60 samples per second). We propose a multi-rate multi-sensor data fusion-based PSDSE framework to utilize the measurements coming from sensors with two different sampling rates. The continuous time-domain nonlinear dynamical and measurement equations are discretized at appropriate sampling periods to obtain two discrete models. Two separate estimators are developed using these models. State information of the intermediate time steps of the estimation having coarser sampling period is evaluated using model-based prediction. These two estimations are optimally combined or fused using Bar–Shalom–Campo formula. The proposed algorithm tracks the dynamic states successfully during transient events such as faults. The method is demonstrated by using the standard IEEE-9, 39, 57, and 118 bus systems. The fusion-based state estimator is shown to perform better than the individual state estimators.