TY - GEN
T1 - Region-based Stability Analysis of Resilient Distribution Systems with Hybrid Grid-forming and Grid-following Inverters
AU - Ding, Lizhi
AU - Men, Yuxi
AU - Du, Yuhua
AU - Lu, Xiaonan
AU - Chen, Bo
AU - Tan, Jin
AU - Lin, Yuzhang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - In this paper, distribution system stability considering mixed grid-forming (GFM) and grid-following (GFL) inverters is discussed. A holistic small-signal model of the distribution feeder accommodating both GFM and GFL inverters is derived. In contrast to conventional point-by-point stability analysis, a region-based stability assessment approach is proposed and implemented to evaluate small-signal system stability with both GFM and GFL controls. Furthermore, to enhance the computational efficiency, an Artificial Intelligence (AI) assisted Kernel Ridge Regression (KRR) approach is developed to obtain the stability region boundary. Therefore, with the derived stability region, the impact of critical parameters on system stability is intuitively shown, and more importantly, the relationship between the maximum penetration level and the corresponding feasible range of the selected control parameters can be quantified. The proposed approach has been validated using MATLAB/Simulink.
AB - In this paper, distribution system stability considering mixed grid-forming (GFM) and grid-following (GFL) inverters is discussed. A holistic small-signal model of the distribution feeder accommodating both GFM and GFL inverters is derived. In contrast to conventional point-by-point stability analysis, a region-based stability assessment approach is proposed and implemented to evaluate small-signal system stability with both GFM and GFL controls. Furthermore, to enhance the computational efficiency, an Artificial Intelligence (AI) assisted Kernel Ridge Regression (KRR) approach is developed to obtain the stability region boundary. Therefore, with the derived stability region, the impact of critical parameters on system stability is intuitively shown, and more importantly, the relationship between the maximum penetration level and the corresponding feasible range of the selected control parameters can be quantified. The proposed approach has been validated using MATLAB/Simulink.
KW - grid-following
KW - grid-forming
KW - kernel ridge regression
KW - small-signal stability
KW - stability region
UR - http://www.scopus.com/inward/record.url?scp=85097172629&partnerID=8YFLogxK
U2 - 10.1109/ECCE44975.2020.9236196
DO - 10.1109/ECCE44975.2020.9236196
M3 - 会议稿件
AN - SCOPUS:85097172629
T3 - ECCE 2020 - IEEE Energy Conversion Congress and Exposition
SP - 3733
EP - 3740
BT - ECCE 2020 - IEEE Energy Conversion Congress and Exposition
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020
Y2 - 11 October 2020 through 15 October 2020
ER -