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In this paper we present an online wide-area oscillation damping control(WAC) design for uncertain models of power systems using ideas fromreinforcement learning. We assume that the exact small-signal model of thepower system at the onset of a contingency is not known to the operator and usethe nominal model and online measurements of the generator states and controlinputs to rapidly converge to a state-feedback controller that minimizes agiven quadratic energy cost. However, unlike conventional linear quadraticregulators (LQR), we intend our controller to be sparse, so its implementationreduces the communication costs. We, therefore, employ the gradient supportpursuit (GraSP) optimization algorithm to impose sparsity constraints on thecontrol gain matrix during learning. The sparse controller is thereafterimplemented using distributed communication. Using the IEEE 39-bus power systemmodel with 1149 unknown parameters, it is demonstrated that the proposedlearning method provides reliable LQR performance while the controller matchedto the nominal model becomes unstable for severely uncertain systems.

Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning