基于粒子滤波和多变量时间序列异常检测的图注意网络检测,用于智能电网中的自动电压控制攻击

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-08-04 DOI:10.1016/j.segan.2024.101494
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引用次数: 0

摘要

自动电压控制(AVC)攻击是一种新型攻击,其目标是调度中心发送给发电机的电压控制指令。成功的自动电压控制攻击可在不被发现的情况下操纵发电机的无功功率或终端电压,导致试点母线的电压偏离从调度中心接收的参考值。这对电力系统的安全稳定运行构成了威胁。本文提出了一种基于粒子滤波(PF)和图注意网络多变量时间序列异常检测(MTAD-GAT)的检测方法。虽然每种方法都能独立检测 AVC 攻击,但两种方法的协调使用会更加有效。PF 和 MTAD 可用于预测试点总线下一时刻的电压变化。将这两种方法结合起来,采用自适应权重,就能计算出自适应混合预测。如果先导母线电压与参考值之差的绝对值超过了根据峰值过阈值(POT)理论自动选择的阈值,则该时刻可确定为攻击时刻。通过在 IEEE 39 总线 6 分区协调二次电压控制 (CSVC) 系统上进行仿真,验证了所提方法的有效性。
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A detection based on particle filtering and multivariate time-series anomaly detection via graph attention network for automatic voltage control attack in smart grid
The Automatic Voltage Control (AVC) attack is a novel attack that targets voltage control instructions sent to generators from the dispatching center. A successful AVC attack can manipulate reactive power or terminal voltage of generators without being detected, causing the voltages of pilot buses to deviate from the reference values received from the dispatching center. This poses a threat to the safe and stable operation of power systems. This paper proposed a detection based on Particle Filtering (PF) and multivariate time-series anomaly detection via graph attention network (MTAD-GAT). Although each method can detect AVC attacks independently, the coordination of the two methods can be more effective. PF and MTAD are utilized to predict the voltage changes of the pilot bus in the next moment. To combine them, adaptive weights are employed, and an adaptive hybrid prediction can be calculated. The moment can be identified as attacked if the absolute value of the difference between the pilot bus voltage and the reference value exceeds a threshold automatically chosen by Peaks Over Thresholds (POT) theory. The proposed method has been validated through simulations on the IEEE 39-bus 6-partition Coordinated Secondary Voltage Control (CSVC) system and has shown to be effective.
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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