Optimal PV Sources Integration in Distribution System and Its Impacts on Overcurrent Relay Based Time-Current-Voltage Tripping Characteristic
Bibliographic record
Abstract
A big interest in the last few decades was about the integration of multiple PV sources-based DG units (PV-DG) into Electrical Distribution System (EDS), and this is for their benefits in enhancing power system reliability and operation. To reach the maximum of those benefits, an optimal location and sizing of PV-DG sources into EDS should be properly designed and developed. This paper consists to apply various algorithms of Particle Swarm optimization (PSO) and choosing the best among them to optimally locate and size the PV-DG sources and identify their impact on protection system in EDS, using a Multi Objective Functions (MOF) that aim for minimizing the three technical parameters of Total Active Power Loss (TAPL), Total Voltage Deviation (TVD), and Total Operation Time (TOT) of Non-Standard Over-Current Relays (NS-OCR) based new time-current-voltage tripping characteristic. This work was applied on the two test systems, the IEEE 33-bus, and the IEEE 69-bus. The obtained results show that, the optimal location and sizing of PV-DG sources in the EDS deliver a positive impact in terms of minimizing the active power losses and improving the voltage profile, but a negative one in the part of system protection, exactly the overcurrent relays coordination.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".