Unified Sensitivity Analysis of Powers and Distortion Quantities in Non-Sinusoidal Regime
Bibliographic record
Abstract
Non-sinusoidal operation is very common in modern power networks and affects both equipment and system-level performance. Real situations involve significant changes in the quantities values of electrical networks and determining the limitations imposed by industrial equipment is particularly important. In this case sensitivity analysis can be extremely useful. This paper unifies two complementary approaches-sensitivity-based evaluation of reactive/apparent ($Q / S_{a}$) power and sensitivity-based evaluation of active power (P) and total voltage and current harmonic distortion (THD${ }_{\text {u }}$, and THD${ }_{\text {i }}$) where also included the power factor$\left(\mathrm{K}_{\mathrm{P}}\right)$-into a single methodology. For these key quantities of nonsinusoidal regime$\mathbf{P}$,$Q, S_{a}, K_{p}, T H D_{u}$, and THD new relations are introduced with respect to harmonic weightings of voltage ($r_{k}$) and current ($p_{k}$). Thus, the modification of one or several parameters simultaneously$\mathrm{r}_{\mathrm{k}}$or/and$\mathrm{p}_{\mathrm{k}}$is reflected in the calculated sensitivities. The second order sensitivities are used to analyze the values of key quantities when two set of harmonic weightings are changed. A compact algorithm based on MATLAB is provided and the errors between the values obtained by classical definition and by sensitivity relations are calculated. The obtained results validate the accuracy of proposed method and the harmonic mitigation solutions reflected by the values of THD${ }_{\mathrm{u}}$, and THD${ }_{\mathrm{i}}$is compliant with IEEE Std 519-2022.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".