基于相量测量的状态估计攻击检测方法
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南瑞集团(国网电力科学研究院)有限公司

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国家自然科学基金


Cyber Attack Detection Method Aiming at Phasor Meaurement State Estimation
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NARI Group

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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    随着通信技术在智能电网中的广泛应用,信息攻击对电网安全运行的威胁也随之而来。电力系统中基于相量测量技术状态估计的攻击方式难以被成功检测到。针对该问题,本文提出了一种面向电力系统基于相量测量数据状态估计的攻击智能检测方法。该方法先采用自编码器对电网测量数据进行特征提取,通过多次提取特征,逐渐降低特征维度。最终的提取信息通过softmax层进行有监督学习,从而得到基于堆叠自编码器的攻击检测算法。针对自编码器的过度拟合问题,由进一步提出基于降噪自编码的攻击检测方法。采用IEEE-118节点测试系统对所提出的方法进行仿真验证,结果表明所提出的攻击检测方法计算精度和效率高于其它方法。

    Abstract:

    With the wide application of communication technology in the smart grid, the threat of information attack to the safe operation of the grid also follows. Attacks based on state estimation using phasor measurement techniques in power systems are difficult to detect successfully. Aiming at this problem, this paper proposes an intelligent attack detection method based on phasor measurement data state estimation for power systems. In this method, the self-encoder is used to extract the features of the power grid measurement data, and the feature dimension is gradually reduced by extracting the features multiple times. The final extracted information is subjected to supervised learning through the softmax layer, resulting in an attack detection algorithm based on stacked autoencoders. Aiming at the overfitting problem of autoencoders, an attack detection method based on noise reduction autoencoders is further proposed. The proposed method is simulated and verified by IEEE-118 node test system. The results show that the proposed attack detection method has higher computational accuracy and efficiency than other methods.

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历史
  • 收稿日期:2022-09-13
  • 最后修改日期:2022-10-24
  • 录用日期:2022-10-26
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