R. Grover Brown

Application of Kalman Filtering in Computer Relaying

During the first cycle following a power system fault, a high speed computer relay has to make a decision usually based on the 60 Hz information, which is badly corrupted by noise. The noise in this case is the nonfundamental frequency components in the transient current or voltage, as the case may be. For research and development purposes of computer relaying techniques, the precise nature of the noise signal is required. The autocorrelation function and variance of the noise signal was obtained based on the frequency of occurrence of the different types of faults, and the probability distribution of fault location. A new technique for modelling the signal and the measurements is developed based on Kalman Filtering theory for the optimal estimation of the 60 Hz information. The results indicate that the technique converges to the true 60 Hz quanitities faster than other algorithms that have been used. The new technique also has the lowest computer burden among recently published algorithms and appears to be within the state of the art of current microcomputer technology.

Observability, Eigenvalues, and Kalman Filtering

In higher order Kalman filtering applications the analyst often has very little insight into the nature of the observability of the system. For example, there are situations where the filter may be estimating certain linear combinations of state variables quite well, but this is not apparent from a glance at the error covariance matrix. It is shown here that the eigenvalues and eigenvectors of the error covariance matrix, when properly normalized, can provide useful information about the observability of the system.

Adaptive Kalman Filtering in Computer Relaying: Fault Classification Using Voltage Models

This paper describes a new probabilistic technique for fault classification to be used in digital distance protection of power systems. The new technique is based on an adaptive Kalman filter using voltage measurements. The voltage data of each phase is processed in two Kalman filter models simultaneously. One Kalman filter assumes the features of a faulted phase while the other has the features of an unfaulted phase. The condition of the phase, faulted or non-faulted, is then decided from the computed a posteriori probabilities.