Two-stage cooperative modular-based approach for solving online optimal power flow problems
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Bibliographic record
Abstract
Various optimal power flow (OPF) algorithms have been utilized in power networks primarily for cost reduction, profit optimization and/or system loss minimization. However, these optimization solvers are either computationally demanding or sensitive to initialization settings and do not guarantee a global optimal. In order to overcome these challenges, this paper proposes an artificial intelligent based optimal power flow (AI-OPF) solver that makes use of a learning architecture of specialized cooperative neural networks (NNs) modules to reproduce a relationship between input and output variables for different loading/generation conditions without using OPF. The proposed algorithm takes advantage of the high computational speed of NN and employs semidefinite programming for the training stage, in order to capture the global optimality property of the technique. The algorithm consists of two stages: the first stage is a classification process which analyzes the loading conditions on the network and assigns it to a pre-identified class; and the second stage performs a class-specific optimal power flow. The proposed optimization approach is suitable for microgrid real-time applications, wherein the operation setup (bus voltages, line flows, etc.) changes rapidly. Numerical tests on a 30-bus system demonstrate the ability of the proposed algorithm to outperform the well-known OPF-based algorithms of MatPower package in terms of computational time and accuracy to attain near-global optimal solutions.
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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) | 0.000 | 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 it