Hybrid Machine Learning-Based Optimization of SVC Firing Angles: A CatBoost-LightGBM Approach for Enhanced Grid Stability
Bibliographic record
Abstract
Flexible AC Transmission Systems (FACTS) have become indispensable tools for enhancing power grid reliability, stability, and efficiency in the modern power grid. Static Var Compensators (SVCs), one of the most useful classes of FACTS devices, play a critical role in compensating reactive power that can adjust voltage levels dynamically. However, this performance is highly related to the SVC’s firing angle unit, which determines the amount of reactive power injection/absorption. Any suboptimal firing angle can lead to grid instability, like voltage collapse and excessive current fluctuations. This paper presents a Simulink model with a hybrid Machine Learning approach combining CatBoost and LightGBM with lagged features to improve firing angle prediction across various scenarios. Despite the usage of single methods, this hybrid one benefits from the strength of previous models that, in the end, demonstrates better performance, faster coverage, and enhanced real-time adaptability among traditional models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".