Application of compressive sensing for distributed and structured power line outage detection in smart grids

Fast and accurate identification and localization of power line outages is one of the critical issues for efficient monitoring and control tasks in future smart grids. In this work, considering the whole power network (PN) as a single graph, the recently introduced sparse formulation of the Power line Outage Identification (POI-SRP) is represented. Our main contribution is to improve and generalize the POI-SRP results for single and multiple line outage identification using matrix analysis and the structured pattern in multiple POI. We address the high coherence and high correlation issues in corresponding POI-SRP sensing matrices. In order to solve these problems, we perform a comprehensive study on the corresponding matrices of the IEEE standard networks in POI-SRP. Our main approach will be based on the necessary and sufficient conditions that these matrices should satisfy in order to be applicable to the Sparse Recovery Problem (SRP). First, we discuss the effect that node-line connection structures in the PN model have on the coherence. We describe a modification of the PN model in order to decrease the coherence. Next, using the IEEE standard 118-BUS as a case study, we discuss how the high correlation problem can be solved by applying mathematical matrix analysis such as matrix decomposition and improving the final POI results. The third main contribution is the identification of the structured outages in POI-SRP. In this work, the boundary conditions of the Clustered OMP recovery algorithm are modified (MCOMP) and finally it will be shown how the existence of structured sparsity in multiple POI problems (Structured-POI-SRP) helps MCOMP to improve the POI results.