Paper Info
Title
Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm
Abstract
Existing generator parameterization methods, typically developed for large turbine generator units, are difficult to apply to small kW-level diesel generators in microgrid applications. This article presents a model parameterization method that estimates a complete set of kW-level diesel generator parameters simultaneously using only load-step-change tests with limited measurement points. This method provides a more cost-efficient and robust approach to achieve high-fidelity modeling of diesel generators for microgrid dynamic simulation. A two-stage hybrid box-constrained Levenberg-Marquardt (H-BCLM) algorithm is developed to search the optimal parameter set given the parameter bounds. A heuristic algorithm, namely Generalized Opposition-based Learning Genetic Algorithm (GOL-GA), is applied to identify proper initial estimates at the first stage, followed by a modified Levenberg-Marquardt algorithm designed to fine tune the solution based on the first-stage result. The proposed method is validated against dynamic simulation of a diesel generator model and field measurements from a 16kW diesel generator unit.
Year
DOI
Venue
2021
10.1109/TSG.2020.3026617
IEEE Transactions on Smart Grid
Keywords
DocType
Volume
Diesel generators,model parameterization,microgrid,dynamic simulation,Levenberg-Marquardt
Journal
12
Issue
ISSN
Citations 
2
1949-3053
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Qian Long15710.30
Hui Yu201.01
Fuhong Xie300.68
Ning Lu414.41
David Lubkeman562.04