Electric Vehicle Switching Attacks Against Subsynchronous Stability of Power Systems
Bibliographic record
Abstract
The deployment of electric vehicles (EVs) requires the integration of information and communication technologies, making power grids prone to cyber threats from EV cyber-infrastructure. On this basis, this paper studies the impact of a new family of EV-based load-altering attacks (EV-LAA) against the subsynchronous stability of the power grid. First, the cyber-physical connections between the EV ecosystem and the power grid are discussed to represent a threat model for coordinated electric vehicle switching attacks (EVSAs) that can excite torsional modes of the system. Then, it will be demonstrated that a traditional proportional-integral (PI)-based subsynchronous resonance damping controller (SSRDC) cannot stabilize the power grid. With the help of a customized unknown input observer (UIO), an adaptive control framework is developed based on a model predictive control (MPC). This framework can generate online control signals and add them to the internal control framework of the synchronous generators (SGs). A modified IEEE Second Benchmark (M-IEEE-SBM) is used to demonstrate the EV-LAAs' consequences and evaluate the effectiveness of the developed adaptive technique. The proposed strategy is also studied through real-time simulations under a testbed that integrates a virtual sphere (vSphere) for an EV ecosystem with power grids simulated in a real-time simulator (i.e., OPAL-RT 5650). To demonstrate the feasibility of this switching attack vector in an actual power system and its impact on SSR stability, the Palo Verde Nuclear Generating Station (PVNGS) is also simulated in this real-time simulator, and the effectiveness of the proposed adaptive control framework is validated under the EV-LAAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".