Reduced Measurement-space Dynamic State Estimation (ReMeDySE) for power systems

Abstract

Applying Kalman filtering techniques to dynamic state estimation is a developing research area in modern power systems. Compared to traditional steady state estimators, the Kalman filter is able to track dynamic state variables both efficiently and accurately. However, in large-scale and wide-area interconnected power systems, the combination of computational complexity-primarily due to the very large number of measurements-and slow processing speeds present a significant challenge. To help address this challenge we have developed an approach we call Reduced Measurement-space Dynamic State Estimation (ReMeDySE). We present the method in the context of the Kalman filter, however it can also be applied to other state estimation methods such as particle filters. In addition, although we present the method in the context of power systems, it is suitable for real-time and massive calculations in any large-scale state tracking systems. Finally, the method lends itself well to modern parallel computation techniques for further improvements.