Open AccessDevelopment and application of measurement data pre-processing tools for parameter identification considering q-axis voltage
Author(s) -
Dunxiang Sun,
Lei Cui
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2087/1/012068
Subject(s) - identification (biology) , computer science , fault (geology) , data processing , voltage , estimation theory , model parameter , control theory (sociology) , data mining , control engineering , algorithm , engineering , artificial intelligence , control (management) , electrical engineering , botany , seismology , biology , geology , operating system
At present, when model parameter identification is carried out, measurement data from phase measurement units or fault recorders are generally used directly. These two types of devices can directly provide the fundamental positive sequence quantities required for parameter identification, but cannot output the dq components. If these measurement data can be fully utilized for parameter identification, it is very beneficial to improve the model accuracy. In this paper, according to the engineering needs of load model parameter identification, the extraction method and variation law of dq components are studied, and the data pre-processing tool is developed and put into use.