Hierarchical-decentralized scheme for short-term voltage stability status prediction and localization
Bibliographic record
Abstract
A hierarchical-decentralized short-term voltage stability (SVS) status prediction and localization scheme is proposed. This scheme can provide SVS status predictions and localization weightages to identify zones with the highest risk of instability, based on the measurements from each zone. • Partitioned the power system into zones to implement the proposed decentralized SVS status prediction scheme considering the electrical coupling between the buses. • Developed a data driven hierarchical-decentralized real-time SVS status assessment scheme which uses Temporal Convolution Network (TCN) classifiers at zonal level and a soft voting algorithm at the central unit. • Proposed and tested a mechanism to rank the most affected zones where counter actions should be initiated. • Conducted a thorough case study that validates all aspects of the proposed SVS status prediction and localization scheme, including investigation of the influence of inverter-based resources (IBR). Data-driven short-term voltage stability (SVS) status assessment is gaining attention due to advancements in deep learning and promising prediction accuracies. Generally, providing support to preserve voltage stability is more effective when it is provided through local countermeasures. Hence, a decentralized approach for predicting short-term voltage stability status using regional measurements can help localization of regions for initiating remedial actions. This paper proposes a hierarchical-decentralized scheme that can predict SVS status and provide localization weightages to identify the zones with the highest risk of instability. The system is partitioned into different zones and a Temporal Convolution Network (TCN) is trained to predict the SVS status of each zone using the zonal measurements. Additionally, a zonal index that indicates the severity of SVS status is computed to help localization scheme. Finally, these locally obtained indicators are conveyed to a central location, which ensembles them to obtain the final decision. This scheme is evaluated on IEEE Nordic test system, modified to incorporate inverter-based resources and dynamic loads, to analyze the performance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".