scholarly journals Multi-machine equivalent model parameter identification method for double-fed induction generator (DFIG)-based wind power plant based on measurement data

2017 ◽  
Vol 2017 (13) ◽  
pp. 1550-1554 ◽  
Author(s):  
Kui Luo ◽  
Wenhui Shi ◽  
Jixian Qu
Keyword(s):  
Power Plant ◽  
Parameter Identification ◽  
Wind Power ◽  
Measurement Data ◽  
Equivalent Model ◽  
Induction Generator ◽  
Wind Power Plant ◽  
Model Parameter ◽  
Identification Method ◽  
Model Parameter Identification

Parameter Estimation of Wind Power Plant Equivalent Model through a Hybrid Method

2020 ◽  
Author(s):  
Gabriel J. N. Gomes ◽  
Elmer P. T. Cari
Keyword(s):  
Power Plant ◽  
Wind Power ◽  
Hybrid Method ◽  
Measurement Data ◽  
Estimation Method ◽  
Optimization Method ◽  
Population Based ◽  
Equivalent Model ◽  
Generic Model ◽  
Wind Power Plant

Reliable models are vital for dynamic simmulations made by electric system operators. Generic models can be adjusted to match certain equipment criteria and provide accurate responses to different faults and disturbances. This paper adresses that issue by proposing a hybrid estimation method to estimate parameters of a wind power plant generic model. At first, the parameters are estimated through the Mean-Variance Mapping Optimization method, a population-based metaheuristic. When the parameters are close enough to their real values, Trajectory Sensitivity method is applied to improve the results and optimize solution. Combining both methods results in a fast and robust estimation approach. The proposed hybrid method was executed using measurement data acquired from PowerFactory and the results show the adequacy of this application.

scholarly journals Development and application of measurement data pre-processing tools for parameter identification considering q-axis voltage

2021 ◽  
Vol 2087 (1) ◽  
pp. 012068
Author(s):  
Dunxiang Sun ◽  
Lei Cui
Keyword(s):  
Parameter Identification ◽  
Extraction Method ◽  
Phase Measurement ◽  
Measurement Data ◽  
Positive Sequence ◽  
Model Accuracy ◽  
Model Parameter ◽  
Load Model ◽  
Measurement Units ◽  
Model Parameter Identification

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.

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