Real coded genetic algorithm based transmission system loss estimation in dynamic economic dispatch problem
Bibliographic record
Abstract
Estimation of transmission loss is vital in scheduling, optimization and planning of power systems. The conventional transmission loss evaluation methods used in power system scheduling problems are not accurate as the transmission network parameters in the system operator database are erroneous and not updated periodically. The conventional techniques rely on the precise network model. Moreover, loss evaluation gains significant importance as it affects the revenues of several utilities. In this context, this article proposes a method to evaluate transmission losses in a scheduling problem without relying on the network model. The proposed method uses samples of real power generation, consumption and losses collected at various operating conditions. From these data, genetic algorithm based loss coefficients (GALCs) are obtained by minimizing the mean absolute error between actual and calculated loss values using real coded genetic algorithm. Then, GALCs are used to evaluate losses in a dynamic economic dispatch problem and its performance is compared with conventional loss estimation techniques. The proposed GALC is validated on the IEEE 30 bus system and using the real time data of the Ontario power system. The performance analysis is also carried out for change in system operating conditions, transmission network modifications and outages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".