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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Fabrice Labeau Marthe Kassouf El-Nasser S. Youssef |
| Abstract | The rapid adoption of the smart grid's nascent load-management capabilities, such as demand-side management and smart home systems, and the emergence of new classes of controllable high-wattage loads, such as energy storage systems and electric vehicles, magnify the smart grid's exposure to load-altering cyberattacks. These attacks aim at disrupting power grid services by staging a synchronized activation/deactivation of numerous customers' high-wattage appliances. A proper defense plan is needed to respond to such attacks and maintain the stability of the grid, and would include prevention, detection, mitigation, incident response, and/or recovery strategies. In this paper, we propose a solution to detect load-altering cyberattacks using a time-delay neural network that monitors the grid's load profile. As a case study, we consider a cyberattack scenario against demand-side management programs that control the loads of residential electrical water heaters in order to perform peak shaving. The proposed solution can be adapted to other load-altering attacks involving different demand-side management programs or other classes of loads. Experiments verify the proposed solution's efficacy in detecting load-altering attacks with high precision and low false alarm and latency. |
| e-ISSN | 19961073 |
| DOI | 10.3390/en15207807 |
| Journal | Energies |
| Issue Number | 7807 |
| Volume Number | 15 |
| Language | English |
| Publisher | MDPI AG |
| Publisher Date | 2022-10-01 |
| Publisher Place | Switzerland |
| Access Restriction | Open |
| Subject Keyword | Technology Smart Grid Cybersecurity Demand-side Management Peak Shaving Load-altering Attacks Detection |
| Content Type | Text |
| Resource Type | Article |
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