Optimal location of Power Quality Monitors in distribution grids based on MRA methodology
Bibliographic record
Abstract
Distribution grids currently face news paradigms where Power Quality (PQ) has become one of the most important aspects for distribution system operators (DSO) and consumers. To ensure a PQ within the limits defined by international standards, there is a permanent need to monitor all parameters associated with the distributed voltage by the grid. This task is carried out using the installation of Power Quality Monitors (PQM) at strategic points of the grid. The main aim of this paper is to define a methodology to optimize the best location for the PQM installation. To achieve this target the Monitor Reach Area (MRA) matrix is calculated and an Integer Linear Programming (ILP) optimization model was used to find the best solution. Two case studies were carried out, in which residual voltage values were observed when three-phase short circuits are applied to all nodes. The results obtained show the good effectiveness of the developed method, presenting solutions that allow the total monitoring of the studied networks, using the smallest possible number of PQMs. In this way, it is possible for the DSO to keep the network monitored in real-time with huge efficiency gains.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".