A Residual Voltage Data-Driven Prediction Method for Voltage Sag Based on Data Fusion

被引：1

作者：

Zheng, Chen ^{[1
]}

Dai, Shuangyin ^{[1
]}

Zhang, Bo ^{[1
]}

Li, Qionglin ^{[1
]}

Liu, Shuming ^{[1
]}

Tang, Yuzheng ^{[1
]}

Wang, Yi ^{[1
]}

Wu, Yifan ^{[2
]}

Zhang, Yi ^{[2
]}

机构：

[1] State Grid Henan Elect Power Co, Elect Power Res Inst, Zhengzhou 450052, Peoples R China

[2] Fuzhou Univ, Coll Elect Engn & Automat, Fuzhou 350100, Peoples R China

来源：

SYMMETRY-BASEL | 2022年 / 14卷 / 06期

关键词：

voltage sag; residual voltage prediction; data fusion;

D O I：

10.3390/sym14061272

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Voltage sag is the most serious power quality problem in the three-phase symmetrical power system. The influence of multiple factors on the voltage sag level and low computational efficiency also pose challenges to the prediction of residual voltage amplitude of voltage sag. This paper proposes a voltage sag amplitude prediction method based on data fusion. First, the multi-dimensional factors that influence voltage sag residual voltage are analyzed. Second, these factors are used as input, and a model for predicting voltage sag residual voltage based on data fusion is constructed. Last, the model is trained and debugged to enable it to predict the voltage sag residual voltage. The accuracy and feasibility of the method are verified by using the actual power grid data from East China.

引用

页数：9

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