Application of distributed Compressive Sensing to Power System State Estimation

This paper presents an application of distributed Compressive Sensing (CS) for data recovery/reconstruction in Power System State Estimation (PSSE). Transmitted measurements to power system control centers may disappear due to congestion or disconnection in communication links, sensor failures, and cyber-attacks. Consequently, the state estimator may encounter problems. In the proposed method, the identified (Phasor Measurement Unit) PMU bad/missing measurement(s) are reconstructed using CS. Data reconstruction exploits the correlation in both time and space among the PMU measurements using a random projection matrix and a wavelet dictionary. The linear state estimation is then carried out using the available and reconstructed PMU measurements. The proposed method is evaluated on the IEEE 57-Bus transmission system. The capabilities and limitations of the proposed method are also discussed.