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Ensuring the stability of power systems is gaining more attraction today thanever before, due to the rapid growth of uncertainties in load and renewableenergy penetration. Lately, wide area measurement system-based centralizedcontrolling techniques started providing a more flexible and robust control tokeep the system stable. But, such a modernization of control philosophy facespressing challenges due to the irregularities in delays of long-distancecommunication channels and response of equipment to control actions. Therefore,we propose an innovative approach that can revolutionize the control strategyfor damping down low frequency oscillations in transmission systems. Proposedmethod is enriched with a potential of overcoming the challenges ofcommunication delays and other non-linearities in wide area damping control byleveraging the capability of the reinforcement learning technique. Such atechnique has a unique characteristic to learn on diverse scenarios andoperating conditions by exploring the environment and devising an optimalcontrol action policy by implementing policy gradient method. Our detailedanalysis and systematically designed numerical validation prove thefeasibility, scalability and interpretability of the carefully modelledlow-frequency oscillation damping controller so that stability is ensured evenwith the uncertainties of load and generation are on the rise.

Wide Area Measurement System-based Low Frequency Oscillation Damping Control through Reinforcement Learning