Fault Diagnosis in Photovoltaic Systems Using Machine Learning Algorithms
Bibliographic record
Abstract
Driven by environmental concerns, the global energy landscape is undergoing a significant shift from use of fossil fuels to renewable energy sources. Solar energy, particularly through photovoltaic (PV) technology, has emerged as a prominent renewable energy source. However, use of PV systems faces challenges due to fault occurrences, which negatively impact their efficiency and power output. With machine learning revolutionizing the energy sector, expectations hold in offering potential solutions to fault diagnosis challenges in PV systems. This study focuses on utilizing machine learning algorithms (MLA) for fault diagnosis in PV systems. Through a MATLAB simulation of a 7.5 kW PV system, three fault scenarios were implemented to generate the dataset. MLAs were trained and tested using MATLAB classification application, with cross and hold-out validation techniques employed for model validation. Results revealed that the wide neural network algorithm achieved the highest accuracy, reaching 96.30% during training and 98.39% during testing with the cross validation of 10 folds. The ensemble algorithms also demonstrated promising results, achieving accuracies of 93.73% and 94.06% during training and testing, respectively, with cross-validation. These findings underscore the effectiveness of machine learning algorithms in accurately diagnosing faults in PV systems, offering valuable insights for PV system maintenance to ensure efficiency, reliability, availability, and overall performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".