Model-Based Secure Load Frequency Control of Smart Grids Against Data Integrity Attack
Bibliographic record
Abstract
In this paper, a cyber-physical digital signature creation method and a resilient load frequency control approach are proposed to improve the security of the existing SCADA protocols used in the load frequency control of interconnected power systems. In specific, the dynamic model of the power system is used to predict the future states of the system and the statistics of a number of future states are calculated and used as the dynamic hash to determine the integrity of the data communicated between remote telemetry units and the power system SCADA control center. The proposed algorithm has inherently the cyber-physical characteristics and enhanced robustness to collisions without adding a larger overhead to the message frame of the packets in current SCADA protocols. To compensate for the possible performance degradation and instability of the power system caused by corrupted data, a model-based resilient load frequency controller is designed for the smart grid. The maximum interval between two consecutive feedback updates in the model-based control scheme is obtained for securing the stability of the power system. The investigation of three types of integrity attacks on a two-area power system shows that the proposed model-based digital signature attack detection scheme is able to detect even a little inconsistency of the communicated data. By using the designed resilient load frequency controller, the stability and dynamic performance of the smart grid are guaranteed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.002 | 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".