Cybersecurity Vulnerabilities in Phase-Locked Loop (PLL) of DFIG-Based Wind Power Plants
Bibliographic record
Abstract
The integration of wind energy, particularly Doubly-Fed Induction Generator (DFIG) turbines, into power grids has increased significantly owing to their performance and cost-efficiency. Within the DFIG-based turbines, a critical component is the Phase-Locked Loop (PLL). This study delves into the cybersecurity vulnerabilities associated with PLL systems. Initially, a dynamic model for DFIG turbines—including voltage and current control loops and the PLL—is developed. The PLL model is assumed to work based on the fundamental frequency of the positive sequence of voltage. Then, it has been shown that when a malicious signal with the natural frequency of PLL is injected into the measurements, the estimation of phase angle will start to oscillate. This instability, in turn, leads to disruptions in the stability of the active power injected into the grid which results in DFIG disconnection and highly threatens the grid stability. We further investigate the impacts of the proposed attack under varying parameter configurations, employing the EPRI benchmark within the EMTP-RV software.
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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 | 0.000 |
| 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".