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This paper designs an Operator Learning framework to approximate the dynamicresponse of synchronous generators. One can use such a framework to (i) designa neural-based generator model that can interact with a numerical simulator ofthe rest of the power grid or (ii) shadow the generator's transient response.To this end, we design a data-driven Deep Operator Network~(DeepONet) thatapproximates the generators' infinite-dimensional solution operator. Then, wedevelop a DeepONet-based numerical scheme to simulate a given generator'sdynamic response over a short/medium-term horizon. The proposed numericalscheme recursively employs the trained DeepONet to simulate the response for agiven multi-dimensional input, which describes the interaction between thegenerator and the rest of the system. Furthermore, we develop a residualDeepONet numerical scheme that incorporates information from mathematicalmodels of synchronous generators. We accompany this residual DeepONet schemewith an estimate for the prediction's cumulative error. We also design a dataaggregation (DAgger) strategy that allows (i) employing supervised learning totrain the proposed DeepONets and (ii) fine-tuning the DeepONet using aggregatedtraining data that the DeepONet is likely to encounter during interactivesimulations with other grid components. Finally, as a proof of concept, wedemonstrate that the proposed DeepONet frameworks can effectively approximatethe transient model of a synchronous generator.

On Approximating the Dynamic Response of Synchronous Generators via  Operator Learning: A Step Towards Building Deep Operator-based Power Grid  Simulators