Evaluating the Impact of Phasor Measurement Units on the Accuracy of State Estimation
Bibliographic record
Abstract
PMUs are viewed as one of the most vital measurement devices in future of electric grid. PMUs devices can provide synchronized phasor measurement of voltages and currents from broadly scattered areas in an electric power grid. A hybrid Multilayer Perceptron NN-Stochastic Fractal Search (MLP-SFS) algorithm is being proposed in rectangular coordinates to solve hybrid state estimator problem. Hybrid SE is defined based on its measurement set which consists of traditional as well as synchronized measurements. The approach classifies the process into two steps. The first step, Multilayer Perceptron NN is used to compute the initial estimated states. The second step, SFS is implemented to acquire the final estimated states. This hybrid technique is used to improve the accuracy of state estimation. The size of PMUs is gradually increased by adding them to the conventional measurement set. Six cases are tested to show the impact of PMUs on the accuracy. The application of the hybrid technique is illustrated on IEEE 14, 30, and 57-bus systems. The Performance of MLPN-SFS is compared to MLP and SFS individually.
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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 | 0.001 |
| 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".