TY - GEN
T1 - Holistic Small-Signal Modeling and AI-Assisted Region-Based Stability Analysis of Autonomous AC and DC Microgrids
AU - Men, Yuxi
AU - Ding, Lizhi
AU - Du, Yuhua
AU - Lu, Xiaonan
AU - Zhao, Dongbo
AU - Cao, Yue
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - In this paper, a holistic small-signal model of hybrid AC and DC microgrids is developed, including AC subsection, DC subsection, and interface inverters between AC and DC buses. Based on the derived complete small-signal model, a region-based stability analysis approach is proposed and developed. Meanwhile, to obtain the steady-state operating points used in the regionbased stability analysis, practical and effective power flow calculation is conducted for droop-controlled hybrid AC and DC microgrids. Rather than following a conventional point-by-point stability evaluation procedure, the stability region implemented in this work is derived based on the selected cross-domain parameters from either control systems or main power circuits. Furthermore, an artificial intelligence (AI) assisted Kernel Ridge Regression (KRR) algorithm is implemented to derive the stability boundary with enhanced computational efficiency. Simulation tests are presented to demonstrate the effectiveness of the proposed method.
AB - In this paper, a holistic small-signal model of hybrid AC and DC microgrids is developed, including AC subsection, DC subsection, and interface inverters between AC and DC buses. Based on the derived complete small-signal model, a region-based stability analysis approach is proposed and developed. Meanwhile, to obtain the steady-state operating points used in the regionbased stability analysis, practical and effective power flow calculation is conducted for droop-controlled hybrid AC and DC microgrids. Rather than following a conventional point-by-point stability evaluation procedure, the stability region implemented in this work is derived based on the selected cross-domain parameters from either control systems or main power circuits. Furthermore, an artificial intelligence (AI) assisted Kernel Ridge Regression (KRR) algorithm is implemented to derive the stability boundary with enhanced computational efficiency. Simulation tests are presented to demonstrate the effectiveness of the proposed method.
KW - Hybrid AC and DC Microgrids
KW - Kernel Ridge Regression
KW - Small-signal Stability
KW - Stability Region
UR - https://www.scopus.com/pages/publications/85097132984
U2 - 10.1109/ECCE44975.2020.9236022
DO - 10.1109/ECCE44975.2020.9236022
M3 - 会议稿件
AN - SCOPUS:85097132984
T3 - ECCE 2020 - IEEE Energy Conversion Congress and Exposition
SP - 6162
EP - 6169
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 -