Abstract
The Automatic Generation Control (AGC) system is vital for power system frequency stability. The frequency and tie-line power flow are measured and transmitted to the control room to form Area Control Error (ACE), which is then sent to each generator for power generation adjustment. Due to the vulnerability of the Inter-Control Center Communication (ICCP) protocol, which is used for data transmission, many attacks such as Denial of Service, timing de-synchronization, and False Data Injection (FDI) attack can be inflicted upon the compromised system. In this paper, we investigated the attacking mechanism and the impact of the coordinated FDI attack. Compared with the single attack model, the coordinated FDI attack has a smaller Time to Emergency (TTE) value and wider parameter ranges. Therefore, it is stealthier and more harmful to the AGC system. However, it is found that the pattern of the corrupted ACEs (attacked by a specific coordination FDI attack) follows a specific fashion. Therefore, we proposed a self-learning and evolving approach to detect this stealthy attack. The real data from an electric company helps to train and test the pattern recognition model. The coordinated attack is simulated and compared in a 3-area AGC system, while the proposed detection method is verified via the IEEE 39-bus test system.
Highlights
INTRODUCTIONAutomatic Generation Control (AGC) is regarded as the most critical function among three power control measures, i.e., the control via synchronous generator's
We focus on the false data injection attack during measurement data collection
This paper investigates the mechanism of coordinated False Data Injection (FDI) attack in the Automatic Generation Control (AGC) system and its impact; afterward, a novel detection method based on pattern recognition is proposed
Summary
Automatic Generation Control (AGC) is regarded as the most critical function among three power control measures, i.e., the control via synchronous generator's. By stealthily injecting false data into the measurements, the perpetrator can affect ACE value to sabotage the frequency stability and economic power dispatch. While for the measurement data transmission, the widely deployed Inter-Control Center Communication Protocol (ICCP) is vulnerable to many cyber attacks, such as data manipulation, Denial of Service (DoS), timing de-synchronization, and false data injection attacks [10]. False data injection attack is a major threat to the security of the power system It falsifies the original measurement data by hacking into the compromised Intelligent Electronic. This paper investigates the mechanism of coordinated FDI attack in the AGC system and its impact; afterward, a novel detection method based on pattern recognition is proposed. The IEEE 39bus test system, which can be regarded as a specific real 3-area AGC system, is simulated to find the attacked ACE patterns for coordinated attack detection.
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