Application of adaptive Kalman filtering in fault classification, distance protection, and fault location using microprocessors

An adaptive Kalman filtering scheme is presented for estimation of the 60 Hz phasor quantities, fault type identification, distance protection, and fault location. The current and voltage data of each phase are simultaneously processed in two Kalman filter models. One model assumes that the phase is unfaulted, while the other model assumes the features of a faulted phase. The condition of the phase is then decided from the computed a posteriori probabilities. Upon the secure identification of the condition of the phase, the corresponding Kalman filtering model continues to obtain the best estimates of the current or voltage state variables. Upon convergence to highly accurate values, the appropriate current and voltage pairs are selected to decide the zone of the fault and the fault location. The scheme was tested on digitally simulated data. The fault classification was doubly secure using both voltage and current data. The convergence of estimates reached exact values within half a cycle