A Novel Optimal Placement of Multi-Type Sensors for Smart Grids Observability Using an Enhanced Graph Theory Search Algorithm
Bibliographic record
Abstract
This paper presents a novel approach for optimal multi-type sensor placement using an enhanced graph theory search algorithm, aimed at minimizing both capital and operational costs. The proposed approach combines Phasor Measurement Units (PMUs) and Active and Reactive Power Meters (PQMs) to achieve full smart grid observability. Unlike traditional methods, which assume a fixed cost for PMUs regardless of the number of installed current channels, this approach introduces a binary decision variable for each PMU current channel. This variable is integrated into a dynamic cost model that captures both capital and operational expenses, enabling optimized allocation of PMU current channels. Additionally, the enhanced graph theory search algorithm leverages Ohm's and Kirchhoff's Current Laws to identify indirect observability between adjacent buses, expanding the solution space and optimality. Designed for flexibility and adaptability, the proposed method allows easy customization based on user preferences, enabling optimal PMU placement, incorporation of direct and indirect measurements, management of current channel limitations, and incorporation of PQMs. Tested on both transmission and distribution networks, the proposed method demonstrates superior performance in reducing monitoring costs and enhancing adaptability to specific user needs.
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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 | 0.000 |
| 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) | 0.003 | 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".