Optimal multi-type sensor placement using hybrid graph theory and numerical observability analysis for system observability and cybersecurity enhancement
Bibliographic record
Abstract
Phasor Measurement Units (PMUs) and Micro PMUs ( μ PMUs) are among the most promising measuring systems used for smart grid (SG) monitoring. In this paper, a novel method is proposed to find the optimal multi-type sensor placement that achieves full system observability while optimizing measurement redundancy to mitigate the effect of cyberattacks, if any, and maximize the State Estimation (SE) accuracy. The proposed method utilizes the advantages of both graph theory and numerical observability analysis. A Graph Theory-based Engine (GTE) is developed to ensure power system observability under steady-state and contingency conditions. The decision variables of GTE are designed to provide the measurement types and their location simultaneously. This allows for the development of a Numerical Observability-based Engine (NOE), which evaluates the performance of each candidate solution via Monte Carlo Simulation (MCS) in order to maximize the SE accuracy and support the system protection against False Data Injection Attacks (FDIAs). The proposed combined GTE-NOE method is tested using the modified IEEE-33, IEEE-69, and IEEE-39 New England systems. The results show that the combined GTE-NOE method achieves optimal multi-type sensor placement under steady-state and contingency conditions, maximizes SE accuracy, and provides measurement redundancy to mitigate the effect of FDIAs in both distribution and transmission levels.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".