Probabilistic approach for optimal planning of distributed generators with controlling harmonic distortions
Bibliographic record
Abstract
In this study, a probabilistic planning approach is proposed for optimally allocating different types of distributed generator (DG) (i.e. wind‐based DG, solar DG and non‐renewable DG) into a harmonic polluted distribution system so as to minimise the annual energy losses and reduce the harmonic distortions. The proposed planning methodology takes into consideration the intermittent nature of the renewable resources, load profile and the technical constraints of the system. The objective function is the total system annual power loss. The constraints include voltage limits at different buses (slack and load buses) of the system, feeder capacity, total harmonic distortion (THD) limits and maximum penetration limit of DG units. The optimisation process is achieved using the genetic algorithm optimisation method. This proposed approach has been applied to a typical rural distribution system with different scenarios including all possible combinations of distributed energy resources. The simulation results using Matlab programming environment show that significant reductions in the energy losses and THD are achieved for all the proposed scenarios. Also simulation results depict that the proposed method is robust and computationally efficient.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".