Power system state estimation with dynamic optimal measurement selection

Power system measurement devices continue to evolve towards higher accuracy and update rate. On the other hand, the computation required for processing the enormous amounts of measurement data associated with large complex power systems makes real-time estimation a major challenge. In this paper we present the Lower Dimensional Measurement-space (LoDiM) state estimation method for large-scale and wide-area interconnected power systems. We present the method in the context of the Kalman filter and Extended Kalman filter, however our measurement selection procedure is not filter-specific, i.e. it can also be applied on other state estimation methods such as particle filters and unscented filters. Our method can also take advantage of large-scale parallel computation techniques for further improvement. Moreover, the concept of LoDiM should be applicable to other large-scale, real-time and computationally-intensive state tracking systems beyond the power systems, such as weather forecasting systems, gas-pipeline systems, and other critical infrastructure.