Please use this identifier to cite or link to this item:
|A Statistical-Based Approach to Load Model Parameter Identification
|Institute of Electrical and Electronics Engineers Inc.
|A Statistical-Based Approach to Load Model Parameter Identification / A. Gulakhmadov, A. Tavlintsev, A. Pankratov, et al. — DOI 10.1109/ACCESS.2021.3076690 // IEEE Access. — 2021. — Vol. 9. — P. 66915-66928. — 9419005.
|In the last few years, a great number of methods for identifying the load model parameters have been proposed. This article discusses the use of statistical approach to estimate the substation equivalent load model parameters for supplying to oil-producing industrial region. The disadvantages of existing statistical approach are the low accuracy obtained for the parameter estimates, especially when using samples size is small. To eliminate this deficiency, the current measurement data archive from SCADA system of electrical parameters for 15 months was collected. For the purpose of verifying the obtained results of statistical processing of SCADA data, a full-scale experiment was carried out in relation to the studied substation. The article describes the statistical method used to process the current SCADA measurement data, the results of archived statistical processing and experimental SCADA data. The electrical load models' parameters received from the experimental studies results are of practical importance. © 2013 IEEE.
POWER SYSTEM STUDY
STATIC LOAD MODEL
EQUIVALENT LOAD MODEL
|This work was supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences, in part by the Pan-Third Pole Environment Study for a Green Silk Road under Grant XDA20060303, in part by the K. C. Wong Education Foundation under Grant GJTD-2020-14, in part by the International Cooperation Project of National Natural Science Foundation of China under Grant 41761144079, in part by the CAS PIFI Fellowship under Grant 2021PC0002, in part by the Xinjiang Tianchi Hundred Talents Program under Grant Y848041, in part by the CAS Interdisciplinary Innovation Team under Grant JCTD-2019-20, in part by the project of the Research Center of Ecology and Environment in Central Asia under Grant Y934031, and in part by the Regional Collaborative Innovation Project of Xinjiang Uygur Autonomous Regions under Grant 2020E01010.
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