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Smart city article details
| Title | Digital Twins For Sustainable Design And Management Of Smart City Buildings And Municipal Infrastructure |
|---|---|
| ID_Doc | 20294 |
| Authors | Tan Z.; Li Z. |
| Year | 2024 |
| Published | Sustainable Energy Technologies and Assessments, 64 |
| DOI | http://dx.doi.org/10.1016/j.seta.2024.103682 |
| Abstract | In the context of sustainable energy, exemplified by smart grids, bidirectional information transmission unfolds as a key feature. The deep integration in this data-driven model poses heightened risks to infrastructure while enhancing control over automation processes. A significant concern is the false data injection attack (FDIA), which intricately targets sustainable energy system state estimation. This attack allows adversaries to bypass detection methods, manipulate outcomes, and jeopardize secure grid operation. This study explores FDIA identification in smart grids, focusing on Recurrent Neural Networks (RNNs) for detection. The objective is to assess the proposed detection methodology through simulation experiments on the IEEE standard test scheme, validating practicality under diverse sustainable energy conditions. This study undertakes a focused exploration into the realm of identifying FDIAs within smart grids, centering on the utilization of Recurrent Neural Nets (RNNs) as a machine learning approach for detection. The objective is to assess the efficacy of the proposed detection methodology through rigorous imitation experiments conducted on the IEEE standard test scheme. Various contrasting scenarios are meticulously designed to validate the practicality and effectiveness of the detection model under diverse conditions. In the intricate landscape of Fault Detection, Isolation, and Diagnosis in smart grids, the imperative is to recognize and comprehend abnormal conditions or faults within the grid's operational framework. A robust strategy poised to detect FDIAs in Smart Grids involves the strategic implementation of an advanced digital twin known as the “Cyber-Physical Digital Twin for Smart Grid Security.” This digital twin is meticulously engineered to emulate the intricate dynamics of the Smart Grid, seamlessly integrating both its physical and cyber elements. © 2024 Elsevier Ltd |
| Author Keywords | Digital twin modeling; FDIAs; RNNs (RNN); Smart grids; Sustainable energy management |
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