Multi-objective Parameter Optimization of Multiple VSG and Droop Controlled Inverters for Grid-connected and Islanded Operation

Eggers, Malte; Yang, Huoming; Just, Hendrik; Dieckerhoff, Sibylle; Yin, Hang

doi:10.1109/compel.2019.8769648

Cited by 6 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang et al improved the inertia weights and learning factors in the particle swarm optimisation algorithm to achieve dynamic adjustment of the parameters [15]. Artificial intelligence algorithms obtain optimal control parameters according to different objectives, such as power loss [16], system small signal stability [17], and operating costs [18]. These studies were single-objective optimisation or weighting different objectives directly.…”

Section: Introductionmentioning

confidence: 99%

Design of droop controller in islanded microgrids using multi‐objective optimisation based on accurate small‐signal model

Shi

Yin

et al. 2022

IET Power Electronics

View full text Add to dashboard Cite

The inaccuracy of power sharing is a classic problem of droop control when an islanded AC microgrid suffers from high loads and line impedance differences. It degrades system performance and even destroys system stability. This paper originally presents a multi‐objective optimisation droop control method to solve such a problem. And three objective functions are presented according to the characteristics of microgrids. First, a complete small‐signal model of a microgrid in autonomous operation mode is established. Next, this paper derives constraint conditions for the stable microgrid. Then, this paper originally designs three objective functions for constructing a multi‐objective optimisation (MOO) problem to improve control performance. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to obtain the Pareto optimal frontier of the MOO problem. The fuzzy affiliation function method is used to choose the best solution from the generated Pareto optimal frontier. Finally, simulations verified the validity and superiority of the presented method.

show abstract