Parameter Correction for Electromagnetic Transient Simulation Model Based on GMM-PSO Hybrid Algorithm

This paper proposed an electromagnetic transient model parameter calibration method based on GMM-PSO hybrid algorithm. The purpose was to calibrate the parameters of the simulation model based on the measured waveforms and make the behavior curve produced by the model most in line with objective reality. The parameter calibration problem was modeled as an optimization problem. Specifically, found the smallest estimation error among the parameter combinations that met the constraint conditions. Firstly, different from the widely used ordinary least squares, Kalman filter and other model-driven parameter calibration methods, this paper used the idea of parameter random variability in Bayesian analysis. Then, by generating a large amount of simulation data and using GMM as a tool, the relationship between feature quantities and key parameters was modeled as a joint probability distribution. After that, based on the conditional probability invariance of GMM, the mapping of the feature quantity to the parameter was established to obtain the posterior distribution of the parameter, and the accurate value of the parameter was determined by its statistics. Considering the ambiguity of the correction target, a Particle Swarm Optimization was used to perform a closed-loop check on the feature quantity. Finally, a test case was built using the CloudPSS to verify the effectiveness of the algorithm in the correction of high-dimensional parameters. © 2022 Power System Technology Press. All rights reserved.