Aiming at the traditional topology identification based on steady-state operation, a topology identification method considering power system transient data is proposed. Firstly, the power system is dynamically modeled. Through theoretical derivation, the feature vectors that can reflect the topology information are extracted, and the topology identification problem is transformed into a sparse vector recovery problem. Based on compressive sensing theory, the orthogonal matching pursuit algorithm is adopted to solve the sparse recovery problem. Since the identification process is bidirectional, there may be some identification conflicts. For this consideration, an optimization strategy is introduced to improve the original algorithm. The influence of each algorithm parameter on the topology identification performance is then studied. By considering the transient process, a large amount of effective identification data was obtained in only a few processes. Finally, a simulation test on the proposed algorithm on the IEEE standard 22-bus power distribution system is conducted. The results show that the improved algorithm has outperformed the traditional algorithm.
Improved iteratively reweighted least squares algorithms for sparse recovery problem
