基于HHT和GA-BP的电压暂降源定位方法

doi:10.11930/j.issn.1004-9649.202007250

中国电力 ›› 2022, Vol. 55 ›› Issue (3): 97-104.DOI: 10.11930/j.issn.1004-9649.202007250

基于HHT和GA-BP的电压暂降源定位方法

杨桢¹, 马钰超¹, 李丽², 李鑫¹, 马子莹³

1. 辽宁工程技术大学电气与控制工程学院，辽宁兴城 125105;
2. 国网辽宁省电力有限公司阜新供电公司，辽宁阜新 123000;
3. 国网冀北电力有限公司唐山市丰南区供电分公司，河北唐山 063000

收稿日期:2020-08-13 修回日期:2021-09-25 出版日期:2022-03-28 发布日期:2022-03-29
作者简介:杨桢(1980—)，男，副教授，从事智能电网技术及理论研究，E-mail：yangzhen1980219@163.com;马钰超(1996—)，男，硕士研究生，从事信息处理与模式识别研究，E-mail：420332958@qq.com
基金资助:
国家自然科学基金资助项目(深部复合煤岩卸荷破裂热红外辐射机理及多场耦合模型研究，51604141)

A Novel Method for Voltage Sag Source Location Based on HHT and GA-BP

YANG Zhen¹, MA Yuchao¹, LI Li², LI Xin¹, MA Ziying³

1. Faculty of Electrical and Control Engineering, Liaoning Technical University, Xingcheng 125105, China;
2. Fuxin Power Supply Company of State Grid Liaoning Power Co.,Ltd., Fuxin 123000, China;
3. Tangshan Fengnan District Power supply Branch of State Grid Jibei Electric Power Co., Ltd., Tangshan 063000, China

Received:2020-08-13 Revised:2021-09-25 Online:2022-03-28 Published:2022-03-29
Supported by:
This work is supported by National Natural Science Foundation of China (Research on Thermal Infrared Radiation Mechanism of Deep Composite Coal Rock in Fracture under Unloading and Multi-field Coupling Model, No. 51604141)

摘要/Abstract

摘要： 针对传统电压暂降源定位方法准确率低的缺点，提出了基于希尔伯特-黄变换（Hilbert-Huang transform，HHT）HHT和GA-BP的定位方法。用集合经验模态分解（ensemble empirical mode decomposition，EEMD）对故障期间的电压和电流进行处理，得到有效特征值-电流实部Icos θ和系统轨迹斜率k，接着用GA-BP神经网络对有效特征值进行分类找出故障源的初步位置，然后用飞蛾扑火优化（moth-?ame optimization，MFO）算法对故障线路的电压电流构建的数学模型求解，从而对电压暂降源进行精确定位。同时，根据三相电压幅值之间的关系区分故障类型。最后，以双电源系统仿真模型为例对所提方法进行验证，仿真结果表明：提出的定位方法准确率和精度很高，能够准确地定位出电压暂降源的位置。

关键词: 集合经验模态分解, 希尔伯特变换, GA-BP神经网络, 电流实部, 系统斜率轨迹

Abstract: Aiming at the low accuracy of traditional voltage sag source positioning methods, this paper proposes a new location method based on HHT and GA-BP. Firstly, the EEMD(ensemble empirical mode decomposition) Hilbert-Huang Transform (HHT) is used to process the voltage and current during the fault period, and the effective eigenvalues - the current real part Icosθ and system trajectory slope k are obtained. Then the GA-BP neural network is used to classify the effective eigenvalues to get the initial location of the voltage sag source. And then, the moth to flame optimization (MFO) algorithm is used to solve the mathematical model of voltage and current of the fault line, so that the precise location of the voltage sag source is obtained. At the same time, the fault types are distinguished according to the relationship between the three-phase voltage amplitudes. Finally, a simulation model of a dual power supply system is used to verify the proposed method, and the simulation results show that the proposed method has high accuracy and precision in positioning and can precisely locate the voltage sag source.

Key words: ensemble empirical mode decomposition, Hilbert transformation, GA-BP neural network, real part of current, the slope trajectory of the system

杨桢, 马钰超, 李丽, 李鑫, 马子莹. 基于HHT和GA-BP的电压暂降源定位方法[J]. 中国电力, 2022, 55(3): 97-104.

YANG Zhen, MA Yuchao, LI Li, LI Xin, MA Ziying. A Novel Method for Voltage Sag Source Location Based on HHT and GA-BP[J]. Electric Power, 2022, 55(3): 97-104.

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基于HHT和GA-BP的电压暂降源定位方法

A Novel Method for Voltage Sag Source Location Based on HHT and GA-BP

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基于HHT和GA-BP的电压暂降源定位方法

A Novel Method for Voltage Sag Source Location Based on HHT and GA-BP

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