Power system state estimation with dynamic optimal measurement selection

Abstract

Abstract-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 Measurementspace (LoDiM) state estimation method for large-scale and widearea interconnected power systems. We present the method in the context of the Kalman lter and Extended Kalman lter, however our measurement selection procedure is not lter-specic, i.e. it can also be applied on other state estimation methods such as particle lters and unscented lters. 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 computationallyintensive state tracking systems beyond the power systems, such as weather forecasting systems, gas-pipeline systems, and other critical infrastructure.

Stabilized Controller Design for Attitude and Altitude Controlling of Quad-Rotor Under Disturbance and Noisy Conditions

“…The time update equations project the state and covariance estimate 'P k+1 ' from previous time step 'k' to the current time step 'k+1' Equation (14) (Zhang et al, 2011).…”

“…EKF lies on the principles of linearization of the current estimation error mean and covariance (Zhang et al, 2011).…”