Harmonic Reducing ANN Controller for a SVC Compensating Unbalanced Fluctuating Loads
Bibliographic record
Abstract
This paper presents the development of an artificial neural network (ANN) controller for a Static Var Compensator (SVC) system compensating unbalanced fluctuating loads. The proposed controller can balance and reduce the reactive power drawn from the source under unbalanced loads while keeping harmonic injection to the system due to SVC operation low. The first stage of controller development is a fuzzy logic based control algorithm. This algorithm determines a number of possible operating states of SVC for a given load condition and calculates corresponding harmonics injections. Then a fuzzy logic system is used to rank those operating states in terms of the magnitude of the reactive power drawn from the source and the harmonic injection level indicated by Total Demand Distortion (TDD). The best operating state is selected based on the ranking score assigned by the fuzzy logic ranking system. Finally, computational speed of the controller is improved by replacing the analytical and fuzzy computations by a set of neural networks trained with data generated with fuzzy logic based controller.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".