Open AccessModel order reduction for transient simulation of active distribution networks
Author(s) -
Wang Chengshan,
Yu Hao,
Li Peng,
Wu Jianzhong,
Ding Chengdi
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.0219
Subject(s) - reduction (mathematics) , krylov subspace , computer science , transient (computer programming) , passivity , model order reduction , stability (learning theory) , control theory (sociology) , state space , mathematical optimization , engineering , algorithm , mathematics , artificial intelligence , iterative method , control (management) , machine learning , projection (relational algebra) , statistics , geometry , electrical engineering , operating system
With the increasing penetration of distributed generation, distribution networks are evolving from passive to active. New transient simulation methods are required to study the detailed dynamic characteristics of large‐scale active distribution networks. A model order reduction method based on Krylov subspace theory is introduced in this study to reduce the overall model scale of active distribution networks for transient simulations. A modified state‐space model of linear distribution networks is developed to replace the conventional models for improvement in model reduction efficiency, while guaranteeing the passivity and stability of the reduced models. The proposed model order reduction method is validated with the IEEE 123‐node test network. The results prove that the proposed method is effective for different applications to improve the simulation efficiency of large‐scale active distribution networks.