A Distributed Optimization Algorithm for Attack-Resilient Wide-Area Monitoring of Power Systems: Theoretical and Experimental Methods

In this paper we present a real-time distributed optimization algorithm based on Alternating Directions Method of Multipliers (ADMM) for resilient monitoring of power flow oscillation patterns in large power system networks. We pose the problem as a least squares (LS) estimation problem for the coefficients of the characteristic polynomial of the transfer function, and combine a centralized Prony algorithm with ADMM to execute this estimation via distributed consensus. We consider the network topology to be divided into multiple clusters, with each cluster equipped with a local estimator at the local control center. At any iteration, the local estimators receive Synchrophasor measurements from within their own respective areas, run a local consensus algorithm, and communicate their estimates to a central estimator. The central estimator averages all estimates, and broadcasts the average back to each local estimator as the consensus variable for their next iteration. By imposing a redundancy strategy between the local and the global estimators via mutual coordination, we show that the distributed algorithm is more resilient to communication failures as compared to alternative centralized methods. We illustrate our results using a hardware-in-loop power system testbed at NC State federated with a networking and cyber-security testbed at USC/ISI.