This work develops and benchmarks surrogate models for Dynamic Phasor (DP) simulation of electrical drives. DP simulations of complex systems may be time-consuming due to the increased number of equations. Thus, it is desirable to have a datadriven approach to compute the critical state/control variables and power losses. The surrogate models are intended to be used as a steady-state equivalent of the DP simulation model. We consider the Gaussian Process (GP), Multi Layer Perceptron, and Random Forest as surrogate models. Among other techniques, GPs are found to have good accuracy. Moreover, GPs are dataefficient and have desirable properties, such as built-in uncertainty quantification. The study shows that the GP performs better compared to other techniques in terms of the Mean Squared Error of the prediction, while still being very fast to evaluate. We illustrate the potential of these surrogate models to also predict transient behavior.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.