A Cloud-Based Power Flow Assessment Tool
Bibliographic record
Abstract
Power flow analysis is the most common analysis tool used by power system operators and researchers. The solution of the set of power balance equations, which model an entire power system, reveals the node voltages, line power flows, active and reactive generation power. In this paper, an academic cloud-based power flow tool is proposed. The application described in this paper is developed using the Ruby programming language due to its quick processing times, easy syntax and portability. For application deployment, an Amazon Elastic Compute Cloud (EC2) server is chosen due to its scaling flexibility, computing performance, and minimal ongoing server-side maintenance and resource management. The application itself is developed using a Ruby web framework which handles all web verb requests such as GET and POST. It has been designed with the ability to handle upwards of 1500 users concurrently with no measurable drop in processing times or accuracy in the final results. The processing algorithm implemented is based on the Newton-Raphson power flow method which is discussed in great detail in this paper. The final implementation of this processing algorithm with a 14-bus system demonstrates good performance in terms of processing time and accuracy in comparison to MATPOWER. The application also hosts a plethora of features that enable the users to easily upload and download data as well as visualize their bus and line system and validate their numerical inputs for correctness.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".