Dynamic load modeling for industrial facilities using template and PSS/E composite load model structure CLOD
Bibliographic record
Abstract
Industrial facility loads have significant impact on power system stability. Due to their large power demand and complicated impact on system dynamic performance, it is critical to model them properly. Because the knowledge of industry-specific load composition data are limited, load model accuracy of the current utility practice is greatly compromised. In this paper, a new dynamic load modeling method is proposed by combining a template of a specific type of industrial facilities and the composite load model structure (CLOD) in the commercial software PSS/E. This method is relatively easier to implement in PSS/E and can still achieve reasonable accuracy. The proposed method consists of three steps: 1) create a template by conducting an in-depth load survey for a specific type of industrial facilities; 2) determine load composition of the facility that is required by PSS/E CLOD load model structure using the template; 3) create a PSS/E CLOD load model of the facility using the load composition data. To validate the proposed method, a case study and a sensitivity study are conducted using a real 110-megawatt (MW) Kraft paper mill facility. The case study verifies the accuracy of the proposed model by comparing simulation results with actual field measurements of the 110 MW Kraft paper mill facility; the sensitivity study shows the robustness of the proposed modeling method when subjected to load parameters variation. The proposed method can serve as a generic method for dynamic load modeling of any type of industrial facilities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".