Machine Learning Based Real-Time Monitoring of Long-Term Voltage Stability Using Voltage Stability Indices
Bibliographic record
Abstract
This article presents a machine learning approach to predict the long-term voltage stability margin as represented by the Loadability Margin (LM). LM is an intuitive and easily understandable indicator of voltage stability. The unique feature of the proposed technique is the use of different Voltage Stability Indices (VSI) proposed in the literature as inputs to an ensemble of machine learning models which predict the LM. The VSIs used are carefully selected to include those based on different principles and computable using real time synchrophasor measurements. In addition, the paper presents a methodology to generate training data under different operational conditions and N-1 contingencies to train the machine learning models. The best machine learning algorithm and the categories of input VSIs are selected through a comparative study. These studies were conducted on the IEEE 14 bus system and IEEE 118 bus system and led to the selection of Random Forest Regression machine learning algorithm, and confirmed the accuracy and robustness of the proposed method. The system was implemented on real time PhasorSmart®synchrophasor application platform and validated using RTDS®real-time simulator. The impact of synchrophasor measurement errors on the proposed technique were also analyzed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".