A Multistage Algorithm for Identification of Nonlinear Aggregate Power System Loads
Bibliographic record
Abstract
A multistage identification algorithm for dynamic power system load models is proposed in this paper. The multistage approach is used to address the nonconvexity of the identification problem. Initial stages are used to find good preliminary estimates for the parameters of the model. Specifically, the initial stages are as follows: Equations for dynamic power system loads are discretized using the zero-order hold method and then approximated with a 2nd-order polynomial NARMAX model. Finally, an extended least squares approach is used to estimate the parameters of the NARMAX model, from which initial estimates for the parameters of the original model are obtained. In the final stage, the values found in the initial stages are used as the starting point for a Levenberg-Marquardt optimization routine that computes the optimal parameters. Numerical experiments using data from both simulated and real systems illustrate the computational complexity and accuracy of the proposed algorithm. Curve-fitting experiments are used to justify the polynomial NARMAX approximation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".