  Ensuring the stability of power systems is gaining more attraction today than
ever before, due to the rapid growth of uncertainties in load and renewable
energy penetration. Lately, wide area measurement system-based centralized
controlling techniques started providing a more flexible and robust control to
keep the system stable. But, such a modernization of control philosophy faces
pressing challenges due to the irregularities in delays of long-distance
communication channels and response of equipment to control actions. Therefore,
we propose an innovative approach that can revolutionize the control strategy
for damping down low frequency oscillations in transmission systems. Proposed
method is enriched with a potential of overcoming the challenges of
communication delays and other non-linearities in wide area damping control by
leveraging the capability of the reinforcement learning technique. Such a
technique has a unique characteristic to learn on diverse scenarios and
operating conditions by exploring the environment and devising an optimal
control action policy by implementing policy gradient method. Our detailed
analysis and systematically designed numerical validation prove the
feasibility, scalability and interpretability of the carefully modelled
low-frequency oscillation damping controller so that stability is ensured even
with the uncertainties of load and generation are on the rise.
