Interconnected Network State Estimation Using Randomly Delayed Measurements

In an interconnected power system, the network security and power transfer capability analyses require a complete network base case solution. With the advent of communications among operations control center computers, real-time telemetered data can be exchanged for complete network modeling. Measurement time skew is a consideration when combining large area data received via a data communication network. In order to accommodate the effects of randomly varying arrival of measurement data, this paper presents an implementation of a stochastic extended Kalman filter (EKF) algorithm, which provides optimal estimates of interconnected network states for systems in which some or all measurements are delayed. The method relies on the delay statistics of exchanged data arrival at the control center and the delay is assumed to have binary statistics, i.e., either the measurements arrive in time or they are delayed by one time sample. Performance of the proposed method is compared with that of the standard weighted least square (WLS) technique.