Deep learning-based data forgery detection in automatic generation control
Deep learning-based data forgery detection in automatic generation control
复制标题
自动发电控制中基于深度学习的数据伪造检测
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Qinghua Li
中科院分区：
文献类型：
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作者：
Fengli Zhang;Qinghua Li
自动发电控制（AGC）是电网的关键控制系统.它是根据频率和平衡区域间联络线潮流计算区域控制误差（ACE），进而调整发电量，使电力系统频率保持在可接受的范围内。然而，攻击者可能会注入恶意的频率或联络线潮流测量，以误导AGC进行错误的发电校正，这将损害电网的运行。这种攻击很难被检测到，因为它们不违反物理电力系统模型。在这项工作中，我们提出了基于神经网络和傅立叶变换的算法来检测AGC中的数据伪造攻击。与以往依靠准确的负荷预测来检测数据伪造的工作不同，我们的解决方案只使用现有AGC系统中已有的ACE数据。特别是，我们的解决方案学习ACE时间序列的正常模式，并检测由人工攻击引起的异常模式。在真实的ACE数据集上的测试表明，该方法具有较高的检测精度.
Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Network and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.