LoDiM: A novel power system state estimation method with dynamic measurement selection

For decades, state estimation has been a fundamental aspect of power systems. However for large-scale and wide-area interconnected power systems, the required computation makes real-time on-line estimation a major challenge. In this paper we present a new method we call Lower Dimensional Measurement-space (LoDiM) state estimation. LoDiM is based on the Extended Kalman filter - popular because of its efficiency, robustness, and typical accuracy. LoDiM, which can take advantage of modern parallel computation techniques, may be useful for other large-scale, real-time on-line and computationally-intensive state tracking systems beyond the power systems, such as weather forecasting or gas-pipeline state estimation. Although LoDiM is presented in the context of the Kalman filter, the associated measurement selection procedure is not filter-specific, i.e. it can be used with other state estimation methods such as particle and unscented filters. If desired, robust estimation techniques can also be employed to detect and eliminate outlier measurements.