SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications
In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g. smart grid and microgrids, in order to produce perturbation for training data.
Item Type | Article |
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Uncontrolled Keywords | Adversarial Attacks; Cyber-Physical Security; Detectors; Energy Management; Energy management systems; Hybrid Microgrid; Informatics; Internet of things (IoT); Machine Learning; Microgrids; Prediction algorithms; Resilience; Security |
Subjects |
Engineering(all) > Control and Systems Engineering Computer Science(all) > Information Systems Computer Science(all) > Computer Science Applications Engineering(all) > Electrical and Electronic Engineering |
Date Deposited | 26 Jul 2024 13:25 |
Last Modified | 26 Jul 2024 13:25 |
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- School of Physics, Engineering & Computer Science
- Centre for Climate Change Research (C3R)
- Energy and Sustainable Design Research Group
- Centre for Engineering Research
- Department of Engineering and Technology
- Communications and Intelligent Systems
- Centre for Future Societies Research
- BioEngineering
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