A Dynamic Surrogate Model Technique for Power Systems Modeling and Simulation

Heterogeneous physical systems can often be considered as highly complex, consisting of a large number of subsystems and components, along with the associated interactions and hierarchies amongst them. The simulation of a large-scale, complex system can be computationally expensive and the dynamic interactions may be highly nonlinear. One approach to address these challenges is to increase the computing power or resort to a distributed computing environment. An alternative to improve the simulation computational performance and efficiency is to reduce CPU required time through the application of surrogate models. Surrogate modeling techniques for dynamic simulation models can be developed based on Recurrent Neural Networks (RNN).This study will present a method to improve the overall speed of a multi-physics time-domain simulation of a complex naval system using a surrogate modeling technique. For the purpose of demonstration, a small scale dynamic model of a power system has been developed as a monolithic implementation in Simulink®. The surrogate modeling technique will be evaluated by comparing time dependent responses of the surrogate against the original monolithic with respect to the approximation accuracy and computational performance.