Hybrid Harmonic Estimation Based on Least Square Method and Bacterial Foraging Optimization
Bibliographic record
Abstract
Although many algorithms have been proposed for power system Harmonic Estimation (HE), most of them suffer from slow convergence and low accuracy. In this paper, a new HE strategy with real-time tracking of amplitude and phase angle of each harmonic component is presented. The proposed method employs linear Least Squares (LS) estimator and improved Bacterial Foraging Optimization (BFO) algorithm for phase angle and magnitude estimation, respectively. The proposed method decomposes the harmonics estimation problem into two problems; linear for amplitude estimation and nonlinear for phase estimation. The aim of this paper is to present an efficient and accurate approach for harmonic parameters estimation. Multiple scenarios are considered to evaluate efficiency and accuracy of the proposed technique. Simulation analysis and investigation of the effects on power system are carried out in MAT LAB and PSCAD software.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".