Open AccessHigh‐fidelity large‐signal order reduction approach for composite load model
Author(s) -
Ma Zixiao,
Wang Zhaoyu,
Zhao Dongbo,
Cui Bai
Publication year - 2020
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.2020.0972
Subject(s) - high fidelity , computer science , electric power system , reduction (mathematics) , fidelity , singular perturbation , model order reduction , control theory (sociology) , computational complexity theory , mathematical optimization , electricity , reliability engineering , power (physics) , algorithm , engineering , mathematics , telecommunications , projection (relational algebra) , mathematical analysis , physics , geometry , control (management) , quantum mechanics , electrical engineering , artificial intelligence
With the increasing penetration of electronic loads and distributed energy resources, conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high‐order non‐linear system with multi‐time‐scale property, which poses challenges on power system studies with heavy computational burden. In order to reduce the model complexity, the authors firstly develop a large‐signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow ones to preserve transient characteristics of the former. Then, accuracy assessment conditions are proposed and embedded into the LSOR to improve and guarantee the accuracy of reduced‐order model. Finally, the reduced‐order WECC composite load model is derived by using the proposed algorithm. Simulation results show that the reduced‐order large‐signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.