Open AccessTwo‐step method for the online parameter identification of a new simplified composite load model
Author(s) -
Yu Songtai,
Zhang Shuqing,
Zhang Xinran
Publication year - 2016
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.2016.0367
Subject(s) - identification (biology) , control theory (sociology) , electric power system , computer science , estimation theory , system identification , stability (learning theory) , power (physics) , algorithm , control (management) , data modeling , artificial intelligence , quantum mechanics , database , machine learning , biology , botany , physics
The electrical load is one of the most important parts of power systems. Parameter identification of load model is critical for power system stability analysis. The composite load model, which comprises a static load and a dynamic load, is one of the most widely used models in power system simulation and control. As the load characteristics could change considerably over time, online parameter identification is needed. However, because of the non‐linearity and complexity, online parameter identification for load models remains difficult to achieve, and there is rarely an effective solution. This study proposes a load model simplification and parameter identification method to solve this problem. First, the composite load model is preliminarily simplified by choosing the dominant parameters. Next, the load model is further simplified based on a second‐ordered state equation of the induction motor. Subsequently, a two‐step method for online parameter identification is presented. The first step is the electrical parameter identification based on the multi‐layer searching method proposed in this study. The second step is the mechanical parameter identification via the Newton method. Finally, an example for a typical case system is presented to demonstrate the effectiveness, efficiency and accuracy of the two‐step method.