| Title | A Novel Real-Time False Data Detection Strategy for Smart Grid |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Mukherjee, Debottam, Chakraborty, Samrat, Banerjee, Ramashis, Bhunia, Joydeep |
| Conference Name | 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) |
| Keywords | composability, Deep Learning, False Data Detection, false data injection attack, Human Behavior, Predictive models, pubcrawl, Real-time Systems, reliability, resilience, Resiliency, Smart grid, Smart grids, state estimation, Support vector machines, Training |
| Abstract | State estimation algorithm ensures an effective realtime monitoring of the modern smart grid leading to an accurate determination of the current operating states. Recently, a new genre of data integrity attacks namely false data injection attack (FDIA) has shown its deleterious effects by bypassing the traditional bad data detection technique. Modern grid operators must detect the presence of such attacks in the raw field measurements to guarantee a safe and reliable operation of the grid. State forecasting based FDIA identification schemes have recently shown its efficacy by determining the deviation of the estimated states due to an attack. This work emphasizes on a scalable deep learning state forecasting model which can accurately determine the presence of FDIA in real-time. An optimal set of hyper-parameters of the proposed architecture leads to an effective forecasting of the operating states with minimal error. A diligent comparison between other state of the art forecasting strategies have promoted the effectiveness of the proposed neural network. A comprehensive analysis on the IEEE 14 bus test bench effectively promotes the proposed real-time attack identification strategy. |
| DOI | 10.1109/R10-HTC53172.2021.9641590 |
| Citation Key | mukherjee_novel_2021 |
A Novel Real-Time False Data Detection Strategy for Smart Grid