Integration of Distributed Generation in Medium Voltage Distribution Network Using Fuzzy Logic Controller for Demand Side Management
Bibliographic record
Abstract
In this research simulations have been carried out on IEEE 13 node test distribution feeder to show the impact of adding distributed generations on voltage profiles along with the changing loads in medium voltage distribution network using Power World Simulator 17 software. Equations of power line loss coefficients have been determined to approximate the numerical values of test feeder to compare the best and worst case. Voltage profile and hosting capacity for the case studies have been determined using the data obtained from the simulations. A fuzzy logic controller has been proposed in the 13 node test distribution feeder to determine the amount of DG output to be added in the network on the basis of power demand gap and time of the day. Another fuzzy logic controller is proposed to select the bus where the distributed generations have to be connected using the input of DG amount needed to be installed and distance of generation from the distribution transformer in the distribution network. Fuzzy logic toolbox of Mat lab has been used to perform the simulation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".