Random forest algorithm and multi-source data fusion based on the converter valve electrical characteristics of time-varying law extraction method and condition monitoring technology research
Bibliographic record
Abstract
As the core equipment of high-voltage direct current transmission system, the operation status of the converter valve directly affects the safety of the power grid.In this paper, we first construct a multisource data fusion system to realize the error-free fusion of fault information parameters.Then, combined with the random forest algorithm, the time-varying law of the electrical characteristics of the converter valve based on harmonic theory is extracted.Finally, the collected time-varying laws of electrical characteristics are input into the constructed Random Forest particle swarm optimization model, and the trained model is used to monitor the status of the converter valve.In the simulation experiment, the ±800kV UHV DC transmission system is built by PSCAD/EMTDC software, from which the current waveforms are collected when the converter valve fails, the time domain features of the current are extracted, and the obtained converter feature indicators are selected using the Random Forest algorithm, and 10 important features will be finally identified to construct the converter valve feature indicator set, and input into the Random Forest Particle Swarm Optimization model and the other comparative models for training and testing.The accuracy of this model is 97.5%, which is better than other comparative models.The study provides a high-precision solution for converter valve condition monitoring and effectively extends the application of multi-source data fusion in power equipment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".