Reliability-Based Artificial Neural Network for Improving Voltage Stability in Smart Grids under Environmental Variability
Bibliographic record
Abstract
Environmental variability, including particulate pollution and meteorological fluctuations, poses significant challenges to the voltage stability of modern smart grids. These impacts are especially pronounced in urban areas with dense industrial and vehicular activities, where pollutants reduce the efficiency of renewable energy generation, increase reactive power demand, and degrade sensor network reliability. Although existing modeling methodologies emphasize accuracy in forecasting environmental effects, their performance variability often undermines the robustness required for realworld smart grid operations. This paper introduces a novel reliability-based artificial neural network framework designed to enhance voltage stability by minimizing variability in voltage deviation prediction. The proposed approach integrates environmental forecasting with smart grid control strategies, aiming to maintain stable grid operations under diverse environmental scenarios. Experiments conducted using three benchmark datasets demonstrate the superior generalization and reliability of the proposed model, highlighting its practical application in improving grid voltage control, renewable integration, and sensor-driven energy management.
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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 | 0.004 | 0.000 |
| 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 | 0.001 | 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 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".