Cyber Induced Harmonic Instability of Inverter-Based Renewable Generators
Bibliographic record
Abstract
Extensive integration of inverter-based resources (IBRs) has introduced stability challenges to power grids due to their low-inertia nature and grid-following control configuration. To address these challenges, recent IEEE standards, e.g., IEEE 1547.1-2020, recommended grid-connected IBRs to provide grid-supporting functions, necessitating communication with utility control centers through various protocols. However, deployment of communication protocols exposes grid-connected IBRs to various threats originated from cyber-layer, endangering overall system stability. To make IBRs resilient to cyber-threats, knowledge about possible attacks and relevant consequences is required. On this basis, this paper emphasizes on vulnerability of grid-connected IBRs to a new family of resonance cyber-attacks. In this attack, the adversary induces harmonic instability by launching a resonance false data injection attack (FDIA) on the IBR's communication layer. To this aim, the attacker can take advantage of impedance-based models or frequency scanning approaches to determine the resonance frequency of the IBR with the grid. Through injecting sinusoidal noise within the resonance frequency range into the signals communicating between sensors or grid-supporting functions and IBRs, the adversary excites the resonance modes of the IBR with the grid, leading to instability of the inverter. The simulation results on a LCL-type grid-connected inverter demonstrated that this resonance FDIA can cause extensive current and voltage harmonic distortion at the inverter's point of common coupling (PCC), which can triggers protection devices and lead to inverter trip.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".