False Data Injection Attack(FDIA)is a typical network attack in power systems,which interferes with the state estimation(SE)process by manipulat-ing power data to influence decision analysis in power systems,thereby af...False Data Injection Attack(FDIA)is a typical network attack in power systems,which interferes with the state estimation(SE)process by manipulat-ing power data to influence decision analysis in power systems,thereby affect-ing the normal operation of the Smart Grid.This paper presents a power FDIA detection method based on long time-series prediction.The method employs an improved Informer model built upon the Transformer architecture,optimizing the model structure and introducing novel attention mechanisms to enhance compu-tational efficiency,speeding up model training and data prediction.Simulation experiments on the IEEE-14 node system are conducted,comparing the proposed method with detection methods utilizing other deep learning algorithms such as Transformer.The results validate the effectiveness of the proposed approach,accu-rately detecting tampered attack data and preventing losses caused by erroneous state estimation in power systems.展开更多
Accurate state estimation is critical to wide-area situational awareness of smart grid.However,recent research found that power system state estimators are vulnerable to a new type of cyber-attack,called false data in...Accurate state estimation is critical to wide-area situational awareness of smart grid.However,recent research found that power system state estimators are vulnerable to a new type of cyber-attack,called false data injection attack(FDIA).In order to ensure the security of power system operation and control,a hybrid FDIA detection mechanism utilizing temporal correlation is proposed.The proposed mechanism combines Variational Mode Decomposition(VMD)technology and machine learning.For the purpose of identifying the features of FDIA,VMD is used to decompose the system state time series into an ensemble of components with different frequencies.Furthermore,due to the lack of online model updating ability in a traditional extreme learning machine,an OS-extreme learning machine(OSELM)which has sequential learning ability is used as a detector for identifying FDIA.The proposed detection mechanism is evaluated on the IEEE-14 bus system using real load data from an independent system operator in New York.Apart from detection accuracy,the impact of attack intensity and environment noise on the performance of the proposed method are tested.The simulation results demonstrate the efficiency and robustness of our method.展开更多
基金supported by Shanghai Natural Science Foundation,No.20ZR1455900Science and Technology Project of State Grid ShanghaiMunicipal Electric Power Company,No.52094022003R.
文摘False Data Injection Attack(FDIA)is a typical network attack in power systems,which interferes with the state estimation(SE)process by manipulat-ing power data to influence decision analysis in power systems,thereby affect-ing the normal operation of the Smart Grid.This paper presents a power FDIA detection method based on long time-series prediction.The method employs an improved Informer model built upon the Transformer architecture,optimizing the model structure and introducing novel attention mechanisms to enhance compu-tational efficiency,speeding up model training and data prediction.Simulation experiments on the IEEE-14 node system are conducted,comparing the proposed method with detection methods utilizing other deep learning algorithms such as Transformer.The results validate the effectiveness of the proposed approach,accu-rately detecting tampered attack data and preventing losses caused by erroneous state estimation in power systems.
基金supported by the National Natural Science Foundation of China under Grants.61573300,61833008Natural Science Foundation of Jiangsu Province under Grant.BK20171445Key R&D Program of Jiangsu Province under Grant.BE2016184.
文摘Accurate state estimation is critical to wide-area situational awareness of smart grid.However,recent research found that power system state estimators are vulnerable to a new type of cyber-attack,called false data injection attack(FDIA).In order to ensure the security of power system operation and control,a hybrid FDIA detection mechanism utilizing temporal correlation is proposed.The proposed mechanism combines Variational Mode Decomposition(VMD)technology and machine learning.For the purpose of identifying the features of FDIA,VMD is used to decompose the system state time series into an ensemble of components with different frequencies.Furthermore,due to the lack of online model updating ability in a traditional extreme learning machine,an OS-extreme learning machine(OSELM)which has sequential learning ability is used as a detector for identifying FDIA.The proposed detection mechanism is evaluated on the IEEE-14 bus system using real load data from an independent system operator in New York.Apart from detection accuracy,the impact of attack intensity and environment noise on the performance of the proposed method are tested.The simulation results demonstrate the efficiency and robustness of our method.
