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Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm
arXiv - CS - Systems and Control Pub Date : 2020-09-22 , DOI: arxiv-2009.10425
Qian Long, Hui Yu, Fuhong Xie, Ning Lu, David Lubkeman

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.

中文翻译:

使用混合盒约束 Levenberg-Marquardt 算法进行微电网仿真的柴油发电机模型参数化

现有的发电机参数化方法,通常是为大型涡轮发电机组开发的,很难应用于微电网应用中的小型 kW 级柴油发电机。本文提出了一种模型参数化方法,该方法仅使用有限测量点的负载阶跃变化测试来同时估计一组完整的 kW 级柴油发电机参数。该方法提供了一种更具成本效益和鲁棒性的方法来实现柴油发电机的高保真建模以进行微电网动态仿真。开发了一种两阶段混合框约束 Levenberg-Marquardt (H-BCLM) 算法来搜索给定参数边界的最佳参数集。一种启发式算法,即基于广义对立的学习遗传算法(GOL-GA),用于在第一阶段识别适当的初始估计,其次是改进的 Levenberg-Marquardt 算法,旨在根据第一阶段的结果微调解决方案。所提出的方法针对柴油发电机模型的动态模拟和 16kW 柴油发电机组的现场测量进行了验证。
更新日期:2020-09-28
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