考虑分布式电源接入的配电网攻击检测方法研究

王顺江; 魏荣鹏; 赵斌; 李祎; 韩耀鹏; 于鹏

doi:doi:10.19912/j.0254-0096.tynxb.2024-1094

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考虑分布式电源接入的配电网攻击检测方法研究

更新时间：2025-12-17

- 考虑分布式电源接入的配电网攻击检测方法研究
- Issue 10, Pages: 509-515(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1094
  CLC： M734
- Published Online：31 October 2025，
  
  Published：31 October 2025
- 稿件说明：
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王顺江, 魏荣鹏, 赵斌, et al. 考虑分布式电源接入的配电网攻击检测方法研究[J]. 2025, (10): 509-515.
DOI：

王顺江, 魏荣鹏, 赵斌, et al. 考虑分布式电源接入的配电网攻击检测方法研究[J]. 2025, (10): 509-515. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1094.

摘要

首先

采用变分模态分解对电力系统采集的量测值进行分解

其中通过引入信号能量概念以确定变分模态分解的最优分解模态数。然后

构造小幅扰动攻击向量

并利用皮尔逊相关系数重构量测数据

滤去量测数据中的高频噪声。最后

设计基于注意力机制的双向长短期记忆网络检测模型

将重构的时序量测数据以及攻击向量作为训练集输入并使用测试集验证。通过对比实验对所提方法进行验证

结果表明该方法能有效提高配电网虚假数据注入攻击检测精度。

Abstract

Consequently

a distribution network false data injection attack detection method based on attention mechanism and bidirectional long short-term memory network is proposed. Initially

the measurement data gathered by power system is broken down through variational mode decomposition

introducing the idea of signal energy to identify the most suitable mode number for variational mode decomposition. Subsequently

a small disturbance attack vector is constructed. Reconstruction of the measurement data using the Pearson correlation coefficient to filter out the high-frequency noise in the measurement data. Ultimately

a bidirectional long short-term memory network detection model based on the attention mechanism is designed. The reconstructed time series measurement data and attack vector are used as training set input and verified using the test set. The proposed method is verified by comparative experiments. The results show that this method can effectively improve the detection accuracy of false data injection attacks in distribution networks. This helps enhance the ability of the distribution network to resist attacks and ensures the safe operation of the power system.

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references

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