Abstract
This paper presents a real-time wide-area damping controller design based on a hybrid intelligent and direct method to improve the power system transient stability. The algorithm applied as a nonlinear optimal wide-area damping controller monitors the oscillations in the system and optimally augments the local excitation system of the synchronous generators. First, energy functions and Prony analysis techniques are used to identify these local or interarea oscillations and develop stability or damping performance index at a given time. Second, artificial neural networks are deployed to learn the dynamics of the system and energy functions based on supervised learning to construct an optimal control design. Then, using online reinforcement learning the quadratic objective function based on the stability index is estimated and optimized forward-in-time. Results on the IEEE 68-bus in Power System Toolbox and HYPERSIM real-time simulator show better transient and damping response when compared to conventional schemes and local power system stabilizers.
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