WECC Composite Load Model Parameter Identification Using Evolutionary Deep Reinforcement Learning

2020 ◽  
Vol 11 (6) ◽  
pp. 5407-5417
Author(s):  
Fankun Bu ◽  
Zixiao Ma ◽  
Yuxuan Yuan ◽  
Zhaoyu Wang
2021 ◽  
Vol 2087 (1) ◽  
pp. 012068
Author(s):  
Dunxiang Sun ◽  
Lei Cui

Abstract 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.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Aminjon Gulakhmadov ◽  
Alexander Tavlintsev ◽  
Aleksey Pankratov ◽  
Anton Suvorov ◽  
Anastasia Kovaleva ◽  
...  

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