Observability of power systems based on fast pseudorank calculation of sparse sensitivity matrices

This paper describes a novel approach for the observability analysis in state estimation of large-scale power systems. We draw a one-to-one correspondence of the observability of a network to the rank of a corresponding sensitivity matrix. This general framework is not purely based on topological aspects, but takes into account all electrical quantities of the network and turns out to be very generic and flexible. In order to solve the observability problem, a novel algorithm for very fast "pseudorank" calculations on sparse matrices is developed. This approach allows, on the one hand, to identify equivalence classes of redundant measurements. On the other hand, the algorithm can detect all observable islands and group unobservable states according to their "observability deficiency." Our algorithm bares high potential in coping with unobservable areas: a method is described which incorporates a minimum number of pseudomeasurements to yield observability. The performance of the algorithm is tested on real-world network (with data gained from an underlying ABB MicroSCADA system) and compared to common rank calculation with singular value decompositions on sparse matrices.