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Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems
To this end, this paper presents the Physics-informed Actor-Critic (PI-AC) algorithm for coordination of Virtual Inertia (VI) from renewable Inverter-based Resources (IBRs) in power distribution systems.
Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems
The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification.
Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems
Bayesian Physics-Informed Neural Networks (BPINNs) combine the advantages of Physics-Informed Neural Networks (PINNs), being robust to noise and missing data, with Bayesian modeling, delivering a confidence measure for their output.
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