Phasor measurement unit (PMU) data manipulation attacks (PDMAs) may blind the control centers to the real-time operating conditions of power systems. Detecting these attacks accurately is essential to ensure the norma...
详细信息
Phasor measurement unit (PMU) data manipulation attacks (PDMAs) may blind the control centers to the real-time operating conditions of power systems. Detecting these attacks accurately is essential to ensure the normal operation of power system monitoring and control. Using long-term accumulated historical PMU measurements to train a machine learning model to detect PDMAs has shown promising results. In this paper, deepautoencoder-based anomaly measurers are deployed throughout the power system to build a distributed PDMA detectionframework. The architecture of a deep autoencoder and its training process are introduced. How to convert the historical PMU measurements into data samples for learning is also elaborated upon. Once trained, an anomaly measurer can assess the PDMA existence possibility of the new PMU measurements. By integrating the results of different anomaly measurers, the proposed distributed PDMA detectionframework can detect PDMAs in the whole power system. The effectiveness and detection performance of the framework are discussed through experiments.
The increasing integration of smart grid technologies in multiregional power systems improves efficiency but also exposes them to cyber security threats, especially false data injection attacks. In this paper, we addr...
详细信息
The increasing integration of smart grid technologies in multiregional power systems improves efficiency but also exposes them to cyber security threats,especially false data injection *** this paper,we address this c...
详细信息
ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
The increasing integration of smart grid technologies in multiregional power systems improves efficiency but also exposes them to cyber security threats,especially false data injection *** this paper,we address this challenge by proposing a distributed detection framework tailored for multi-regional power *** centralized approaches are insufficient to cope with the dynamic and decentralized nature of these *** framework aims to simultaneously monitor and analyze multiple regions in real-time,enhancing the system's ability to withstand false data injection *** study outlines the proposed framework and provides simulation results for validation,emphasizing the need for proactive defense mechanisms in safeguarding the data integrity of power systems.
暂无评论