{"title":"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","authors":"","doi":"10.1016/j.segan.2024.101494","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A detection based on particle filtering and multivariate time-series anomaly detection via graph attention network for automatic voltage control attack in smart grid\",\"authors\":\"\",\"doi\":\"10.1016/j.segan.2024.101494\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":56142,\"journal\":{\"name\":\"Sustainable Energy Grids & Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Energy Grids & Networks\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352467724002236\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Energy Grids & Networks","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352467724002236","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
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.
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.