Small signal stability analysis and control parameter optimization of DC microgrid cluster

• Published in: IET power electronics Vol. 17; no. 10; pp. 1378 - 1397
• Main Authors: Zhang, Zifan, Yang, Xiangyu, Zhao, Shiwei, Zeng, Qi, Liang, Zhanhong, Gao, Mengzhen
• Format: Journal Article
• Language: English
• Published: Wiley 01.08.2024
• Subjects: DC transmission networks; DC–DC power convertors; Pareto optimisation; power grids; stability
• DOI: 10.1049/pel2.12692
• ISSN: 1755-4535

Direct current microgrid (DCMG) clusters are gaining popularity in power systems due to their simplicity and high efficiency. However, DCMG clusters are susceptible to minor disturbances due to low system inertia. This paper proposes a method to enhance the small‐signal stability of a DCMG cluster by optimizing the main control parameters of the system. This paper presents a small‐signal state‐space model of a DCMG cluster system at the system level, considering a multi‐bus network topology. Then, the control parameters that significantly affect the small‐signal stability of the DCMG are selected using the participation factor method. To enhance the system damping, the Pareto‐optimal frontier of the bi‐objective problem was determined using the elite non‐dominated sorting genetic algorithm (NSGA‐II). The optimal compromise is determined by using the fuzzy membership function method to extract it from the generated Pareto optimal front. The proposed method has been verified on a three‐sub DCMG test system with droop control. This paper proposes a method to improve the small‐signal stability of a DC microgrid (DCMG) cluster by optimizing the main control parameters of the system. This paper establishes a direct current (DC) microgrid system‐level small‐signal state space model with a multi‐bus network topology. Then, the control parameters affecting the DCMG small‐signal stability significantly selected through the participation factor method. To increase the system damping, the Pareto‐optimal frontier of the constructed bi‐objective problem was obtained using the elite non‐dominated sorting genetic algorithm (NSGA‐II). The optimal compromise is extracted from the generated Pareto optimal front by adopting the fuzzy membership function method.