SVM and ELM Based on Binary Grey Wolf Optimization for Feature Selection to Detect Defaults of Grid-Connected PV Systems Under MPPT Mode
Bibliographic record
Abstract
This study provides an innovative structure to diagnose the malfunction of the gridconnected photovoltaic energy systems (GPV), depending on the two bilateral improvements: the gray wolf (BGWO) and the differential development (BDE), as well as choosing the properties and classification of automatic learning (ELM and SVM).The proposed methodology relies on real data and includes the following steps: Firstly, we extract statistical parameters from all data sources.Second, the BGWO and BDE algorithms are used separately to choose the properties and reduce their number.Finally, the Extreme Learning Machine (ELM) and the support machine (SVM) are employed to identify seven main breakdowns, namely: nonhomogeneous partial shading, inverter fault, feedback sensor fault, MPPT controller fault, grid anomaly, open circuit in PV array, and boost converter controller fault.The results obtained indicate that the Extreme Learning Machine (ELM) algorithm associated with the BGWO chosen algorithm provides an optimal input vector that detects faults with high accuracy (99.16%) compared to other approaches.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".