  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 paper 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.
