Surrogate Modelling of Dynamic Phasor Simulations of Electrical Drives

• Published in: IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society pp. 1 - 6
• Main Authors: Bagus Satrio Loka, Nasrulloh Ratu, Karthik Gurumurthy, Sriram, Amevor, Bernard, Monti, Antonello, Dhaene, Tom, Couckuyt, Ivo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.10.2022
• Subjects: Computational modeling; Dynamic phasor; Electric potential; Gaussian Process; Gaussian processes; Induction machine; Industrial electronics; Mathematical models; Permanent magnet synchronous motor; Predictive models; Surrogate modelling; Uncertainty
• ISSN: 2577-1647
• DOI: 10.1109/IECON49645.2022.9968552

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 data-driven 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 data-efficient 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.