  Wide-area damping control for inter-area oscillation (IAO) is critical to
modern power systems. The recent breakthroughs in deep learning and the broad
deployment of phasor measurement units (PMU) promote the development of
datadriven IAO damping controllers. In this paper, the damping control of IAOs
is modeled as a Markov Decision Process (MDP) and solved by the proposed Deep
Deterministic Policy Gradient (DDPG) based deep reinforcement learning (DRL)
approach. The proposed approach optimizes the eigenvalue distribution of the
system, which determines the IAO modes in nature. The eigenvalues are evaluated
by the data-driven method called dynamic mode decomposition. For a given power
system, only a subset of generators selected by participation factors needs to
be controlled, alleviating the control and computing burdens. A Switching
Control Strategy (SCS) is introduced to improve the transient response of IAOs.
Numerical simulations of the IEEE-39 New England power grid model validate the
effectiveness and advanced performance of the proposed approach as well as its
robustness against communication delays. In addition, we demonstrate the
transfer ability of the DRL model trained on the linearized power grid model to
provide effective IAO damping control in the non-linear power grid model
environment.
