  In this paper we present an online wide-area oscillation damping control
(WAC) design for uncertain models of power systems using ideas from
reinforcement learning. We assume that the exact small-signal model of the
power system at the onset of a contingency is not known to the operator and use
the nominal model and online measurements of the generator states and control
inputs to rapidly converge to a state-feedback controller that minimizes a
given quadratic energy cost. However, unlike conventional linear quadratic
regulators (LQR), we intend our controller to be sparse, so its implementation
reduces the communication costs. We, therefore, employ the gradient support
pursuit (GraSP) optimization algorithm to impose sparsity constraints on the
control gain matrix during learning. The sparse controller is thereafter
implemented using distributed communication. Using the IEEE 39-bus power system
model with 1149 unknown parameters, it is demonstrated that the proposed
learning method provides reliable LQR performance while the controller matched
to the nominal model becomes unstable for severely uncertain systems.
