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压缩感知和图卷积神经网络相结合的宽频振荡扰动源定位方法

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  • 国家科技期刊平台
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新能源并网引发的宽频振荡严重威胁电网安全,实现宽频振荡源的在线定位并及时采取抑制措施以保证系统安全稳定尤为必要.为此,提出一种压缩采样和图卷积神经网络相结合的宽频振荡源定位方法,该方法首先在子站对时序的振荡信号进行稀疏采样,获得其低维观测序列,作为节点的时序信息,然后在主站融合系统的拓扑结构捕捉各节点的邻接关系,综合考虑系统振荡的时空特性,运用图卷积神经网络实现振荡源定位.最后利用宽频振荡样本集进行仿真验证,结果表明所提方法在量测数据含有噪声、传输数据缺失以及传输数据偏差的情况下都有较高的定位准确度.
Localization Method of Wide-band Oscillation Disturbance Sources Based on Compressed Sensing and Graph Convolutional Neural Networks
The wide-band oscillation caused by the grid connection of new energy seriously threatens the security of the power grid.It is particularly necessary to realize the online location of the broadband oscillation source and take timely suppression measures to ensure the safety and stability of the system.In this paper,a broadband oscillator location method combining compression sampling and graph convolution neural network is proposed.This method firstly sparsely samples the time-series oscillation signal in the substation to obtain its low dimensional observation sequence as the time sequence information of the node,and then captures the adjacency of each node in the topology structure of the master station fu-sion system,comprehensively considers the time-space characteristics of the system oscillation,and uses graph convolution neural network to locate the oscillation disturbance sources.Finally,the broadband oscillation sample set is used for simulation verification.The results show that the proposed method has high positioning accuracy when the measurement data contains noise,the transmission data is missing and the transmission data is biased.

renewable energywide-band oscillationoscillation source localizationcompressed sensingtime-space characteristicgraph convolutional neural network

王渝红、李晨鑫、周旭、朱玲俐、蒋奇良、郑宗生

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四川大学电气工程学院,成都 610065

新能源发电 宽频振荡 振荡源定位 压缩感知 时空特性 图卷积神经网络

国家重点研发计划国家电网科技项目

2021YFB2400800SGSDDK00WJJS2200092

2024

10.13336/j.1003-6520.hve.20221861

高电压技术
中国电力科学研究院　中国电机工程学会

高电压技术

CSTPCD北大核心
影响因子：2.32
ISSN：1003-6520
年,卷(期)：2024.50(3)
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