Fast adaptive schemes for tracking voltage phasor and local frequency in power transmission and distribution systems

Two learning schemes are proposed for fast estimation of voltage phasor and local frequency deviation. The problem was addressed from a system identification perspective, in opposition to the well-established extended Kalman filtering technique. It is shown that, from a simple nonlinear model of the system voltage which involves only two parameters, the recursive least squares and the least means squares algorithms can each provide dynamic estimates of the voltage phasor. The finite derivative of the phase deviation, followed by a moving-average filter, the leads to the local frequency deviation. A constant forgetting factor included in these algorithms provides both fast adaptation in time-varying situations and good smoothing of the estimates when necessary