Fault-Observability Enhancement in Distribution Networks Using Power Quality Monitors
Bibliographic record
Abstract
As far as continuous supply and reliable power delivery to the distribution system customers are concerned, finding a practical and cost-effective method for locating faults after their occurrence is of high importance. In this study, a method is proposed to determine the optimal number and location of power quality monitors (PQMs) to make the distribution network fault-observable, that means, to be able to locate faults as precisely as possible. Moreover, as placement of PQMs is highly dependent on the network topology, the proposed method considers the most probable configurations of the network for optimization. The error of measuring equipment and its effect on number of PQMs is also taken into consideration. The defined objective functions of this study aim to minimize the cost of installing PQMs while minimizing the number of blind-pairs and maximizing the fault-observability level of the network. These objective functions are optimized using Multi-Objective Particle Swarm Optimization (MOPSO) technique. Additionally, to have a better economic evaluation, two scenarios are defined based on the accuracy class of monitoring equipment. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 123-bus distribution test system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 teacher head, 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".