Unbiased Minimum Variance Filter-Based Generator State Estimation Using PMU Measurements for Unknown Generator Input

Estimating the values of internal states representing the synchronous generators is very much essential in various monitoring and control strategies based on the state-space model. Kalman filter-based techniques are used to estimate the states locally using the input and output measurements of the generator so that the expectation of error is minimum. Wide area power system (WAPS) control requires remote monitoring of these states. Phasor measurement units (PMU) are widely used for remote monitoring and control in WAPS, but the remote measurements are limited to output measurements like voltage, current, and frequency. In the case of Kalman filter-based techniques in addition to the output measurements, input measurements like field and torque input are also required in order to estimate the states. This paper proposes an input invariant filter technique for remote estimation of generator states using the limited PMU measurements, even in the absence of mechanical input torque and field voltage measurements. The performance of the filter under various grid conditions and change in input signals are analyzed in this paper. The effect of measurement error, PMU refresh rate, and execution time are also analyzed.