Enhanced Robustness of State Estimator to Bad Data Processing Through Multi-innovation Analysis
Bibliographic record
Abstract
To enhance the robustness of a power system state estimator to topology errors, bad critical measurements, multiple non-interacting, or interacting bad data (BD), this paper presents a new robust detection method by exploiting the temporal correlation and the statistical consistency of measurements. Particularly, we propose three innovation matrices to capture the measurement correlation and statistical consistency by processing the forecasted states/measurements and the interpolated reliable information from phasor measurement units. The latter is achieved by using a robust generalized maximum-likelihood estimator. We then propose to apply the projection statistics (PS) to the proposed innovation matrices for BD detection. Extensive Monte Carlo simulations and QQ-plots are carried out to obtain an analytical threshold of the statistical test of the PS. Because of the robustness of PS and the enhanced measurement redundancy by the innovations, the proposed method is able to handle various types of BD in both PMU observable and PMU partially observable power systems. Moreover, the proposed method is suitable for parallel implementation, and can be integrated with online applications. Comparison results with existing methods under different BD conditions on IEEE 14-bus, 118-bus, and Polish 2383-bus test systems demonstrate the effectiveness and robustness of the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".