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

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (10) : 509-515. DOI: 10.19912/j.0254-0096.tynxb.2024-1094

考虑分布式电源接入的配电网攻击检测方法研究

王顺江^1,2, 魏荣鹏¹, 赵斌³, 李祎³, 韩耀鹏³, 于鹏²

作者信息 +

RESEARCH ON ATTACK DETECTION METHOD OF DISTRIBUTION NETWORK CONSIDERING HIGH PROPORTION OF DISTRIBUTED GENERATION ACCESS

Wang Shunjiang^1,2, Wei Rongpeng¹, Zhao Bin³, Li Yi³, Han Yaopeng³, Yu Peng²

Author information +

文章历史 +

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.

导出引用

王顺江, 魏荣鹏, 赵斌, 李祎, 韩耀鹏, 于鹏. 考虑分布式电源接入的配电网攻击检测方法研究[J]. 太阳能学报. 2025, 46(10): 509-515 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1094

Wang Shunjiang, Wei Rongpeng, Zhao Bin, Li Yi, Han Yaopeng, Yu Peng. RESEARCH ON ATTACK DETECTION METHOD OF DISTRIBUTION NETWORK CONSIDERING HIGH PROPORTION OF DISTRIBUTED GENERATION ACCESS[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 509-515 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1094

中图分类号： TM734

参考文献

[1] 朱自伟, 黄彪, 裘昕月, 等. 基于平抑风光出力波动的主动配电网优化调度[J].太阳能学报, 2022, 43(5): 90-97.
ZHU Z W, HUANG B, QIU X Y, et al.Optimal dispatching of active distribution network based on smoothing wind and solar power output fluctuations[J]. Acta energiae solaris sinica, 2022, 43(5): 90-97.
[2] 王洁, 葛愿, 汪超, 等. 基于共享储能的主动配电网有功优化研究[J]. 太阳能学报, 2024, 45(11): 99-107.
WANG J, GE Y, WANG C, et al.Research on active power optimization of active distribution network based on shared energy storage[J]. Acta energiae solaris sinica, 2024, 45(11): 99-107.
[3] WU K H, LI J W, ZHANG B, et al.Preventive dispatch strategy against FDIA induced overloads in power systems with high wind penetration[J]. IEEE access, 2020, 8: 210452-210461.
[4] 乐健, 郎红科, 谭甜源, 等. 新型配电系统分布式经济调度信息安全问题研究综述[J]. 电力系统自动化, 2024, 48(12): 177-191.
LE J, LANG H K, TAN T Y, et al.Review of research on information security problems in distributed economic dispatch for new distribution system[J]. proceedings Automation of electric power systems, 2024, 48(12): 177-191.
[5] 杨玉泽, 刘文霞, 李承泽, 等. 面向电力SCADA系统的FDIA检测方法综述[J]. 中国电机工程学报, 2023, 43(22): 8602-8622.
YANG Y Z, LIU W X, LI C Z, et al.A review of FDIA detection methods for power SCADA systems[J]. of the CSEE, 2023, 43(22): 8602-8622.
[6] CHEN S M, ZHANG Q Q, LIN D Y, et al.A class of nonlinear Kalman filters under a generalized measurement model with false data injection attacks[J]. IEEE signal processing letters, 2022, 29: 1187-1191.
[7] 巨云涛, 于燕玲, 张紫枫, 等. 计及坏数据辨识的微网群三相分布式状态估计方法[J]. 高电压技术, 2022, 48(4): 1251-1263.
JU Y T, YU Y L, ZHANG Z F, et al.Three-phase distributed state estimation method of microgrid group considering bad data identification[J]. High voltage engineering, 2022, 48(4): 1251-1263.
[8] HU P F, GAO W G, LI Y F, et al.Detection of false data injection attacks in smart grid based on joint dynamic and static state estimation[J]. IEEE access, 2023, 11: 45028-45038.
[9] 董运昌, 王启明, 曹杰, 等. 基于过采样和级联机器学习的电网虚假数据注入攻击识别[J]. 电力系统自动化, 2023, 47(8): 179-188.
DONG Y C, WANG Q M, CAO J, et al.Identification of false data injection attacks in power grid based on oversampling and cascade machine learning[J]. Automation of electric power systems, 2023, 47(8): 179-188.
[10] 郑瑶, 张颉, 姚文轩, 等. 基于空间特征的电网同步量测虚假数据注入攻击检测[J]. 电力系统自动化, 2023,47(10): 128-134.
ZHENG Y, ZHANG J, YAO W X, et al.Detection of false data injection attacks in power grid synchronization measurement based on spatial features[J]. Automation of electric power systems, 2023, 47(10): 128-134.
[11] QU Z W, YANG J C, WANG Y J, et al.Detection of false data injection attack in power system based on hellinger distance[J]. IEEE transactions on industrial informatics, 2024, 20(2): 2119-2128.
[12] 李鑫, 王娟, 邱亚, 等. 基于VMD的混合储能容量优化配置[J]. 太阳能学报, 2022, 43(2): 88-96.
LI X, WANG J, QIU Y, et al.Optimal configuration of hybrid energy storage capacity based on VMD[J]. Acta energiae solaris sinica, 2022, 43(2): 88-96.
[13] 黄哲洙, 金鹏, 王洋, 等. 含光伏发电的配电网分布式状态估计方法[J]. 太阳能学报, 2021, 42(7): 167-178.
HUANG Z Z, JIN P, WANG Y, et al.Distributed state estimation method for distribution networks with photovoltaic power generation[J]. Acta energiae solaris sinica, 2021, 42(7): 167-178
[14] 魏书珩, 徐俊俊, 吴在军, 等. 针对三相不平衡配电网状态估计的虚假数据注入攻击方法[J]. 高电压技术,2021, 47(7): 2367-2377.
WEI S H, XU J J, WU Z J, et al.False data injection attack method for state estimation of three-phase unbalanced distribution network[J]. High voltage technology, 2021, 47(7): 2367-2377.
[15] 宋雨露, 樊艳芳, 刘牧阳, 等. 基于SC-DNN和多源数据融合的新能源电力系统状态估计方法[J]. 电力系统保护与控制, 2023, 51(9): 177-187.
SONG Y L, FAN Y F, LIU M Y, et al.State estimation method of a new energy power system based on SC-DNN and multi-source data fusion[J]. Power system protection and control, 2023, 51(9): 177-187.
[16] 张琰妮, 史加荣, 李津, 等. 融合残差与VMD-ELM-LSTM的短期风速预测[J]. 太阳能学报, 2023, 44(9):340-347.
ZHANG Y N, SHI J R, LI J, et al.Short-term wind speed prediction based on residual fusion and VMD-ELM-LSTM[J]. Acta energiae solaris sinica, 2023, 44(9): 340-347.
[17] YANG Y, LIU H, HAN L J, et al.A feature extraction method using VMD and improved envelope spectrum entropy for rolling bearing fault diagnosis[J]. IEEE sensors journal, 2023, 23(4): 3848-3858.
[18] 焦丕华, 蔡旭, 王乐乐, 等. 考虑数据分解和进化捕食策略的Bi-LSTM短期光伏发电功率预测[J]. 太阳能学报,2024, 45(2): 435-442.
JIAO P H, CAI X, WANG L L, et al.Bi-LSTM short-term photovoltaic power forecasting considering data decomposition and evolutionary predation strategy[J]. Acta energiae solaris sinica, 2024, 45(2): 435-442.