Global Sensitivity Analysis in Load Modeling via Low-Rank Tensor

被引：13

作者：

Lin, You ^{[1
,2
]}

Wang, Yishen ^{[3
]}

Wang, Jianhui ^{[2
]}

Wang, Siqi ^{[3
]}

Shi, Di ^{[3
]}

机构：

[1] GEIRI North Amer, AI & Syst Analyt Grp, San Jose, CA 95134 USA

[2] Southern Methodist Univ, Dept Elect & Comp Engn, Dallas, TX 75205 USA

[3] GEIRI North Amer, San Jose, CA 95134 USA

来源：

IEEE TRANSACTIONS ON SMART GRID | 2020年 / 11卷 / 03期

关键词：

Load modeling; Tensile stress; Computational modeling; Mathematical model; Parameter estimation; Voltage measurement; Reactive power; Dimensionality reduction; load modeling; parameter estimation; sensitivity analysis; tensor;

D O I：

10.1109/TSG.2020.2978769

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Growing model complexities in load modeling have created high dimensionality in parameter estimations, and thereby substantially increasing associated computational costs. In this letter, a tensor-based method is proposed for identifying composite load modeling (CLM) parameters and for conducting a global sensitivity analysis. Tensor format and Fokker-Planck equations are used to estimate the power output response of CLM in the context of simultaneously varying parameters under their full parameter distribution ranges. The proposed tensor structure is shown as effective for tackling high-dimensional parameter estimation and for improving computational performances in load modeling through global sensitivity analysis.

引用

页码：2737 / 2740

页数：4

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