Application of Compressed Sensing and Sparse Representations for State Estimation in Power Systems (1070)

This work demonstrates how Compressed Sensing methods can be utilized to sense crucial events in power networks. It is shown that power systems events may be analyzed in terms of sparse structures, especially if the probability of their occurrence within the network is low. The work presents several examples for such events a, and demonstrates how those events may be detected and located within large power systems using limited data and few sensors.