Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm

TL;DR

This paper introduces a cost-effective, robust method for accurately modeling small diesel generators in microgrids using a hybrid optimization algorithm that requires minimal measurement data.

Contribution

It develops a novel two-stage hybrid optimization approach combining GOL-GA and Levenberg-Marquardt algorithms for diesel generator parameterization in microgrid simulations.

Findings

Validated with field measurements from a 16kW diesel generator.

Achieved high-fidelity modeling with limited load-step data.

Demonstrated improved robustness and efficiency over traditional methods.

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 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.