Application of State Observers and Filters in Protection and Cyber‐Security of Power Grids
Bibliographic record
Abstract
With the recent surge of interest in advanced monitoring, control, and protection applications, power systems are becoming heavily reliant on measurement data to make operational decisions. As a result, ensuring the accuracy and authenticity of data used for those applications is of paramount importance. Thus, this chapter analyzes the use of state observers and filters for enhancing the accuracy of protection and control applications, as well as for identifying faults and anomalies, e.g., cyber-attacks, in power grids. In order to achieve this goal, first, the basics of state estimation and the design procedure for some important observers and filters are discussed to unveil the capabilities of state estimation in power grids. Then, the role of observers and filters in improving the accuracy and authenticity of data is explained. Finally, through three case studies, the capabilities of observers and filters are demonstrated and their performance is evaluated. In case study 1, it is shown how linear unknown input observers can be used to detect and identify cyber threats and faults against the automatic generation control system. In case study 2, an unknown input Kalman filter is developed to enhance the performance of current transformers for traveling-wave (TW)-based protection applications. In the third case study, an observer is designed for power transformers, which are linear parameter-varying systems, to detect inrush currents and over excitation and differentiate these events from internal faults. These case studies clearly demonstrate that how observers and filters can enhance the cyber-security and reliability of power grids.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".