Comparative Analysis of Machine Learning Algorithms for Identifying Partial Shading Conditions on PV Array
Bibliographic record
Abstract
The rapid global expansion of photovoltaic (PV) technologies has made solar power systems a pivotal component in reducing CO2 emissions and enhancing energy security. However, the efficiency of PV systems is often compromised by partial shading, which can significantly reduce power output and lead to accelerated degradation of solar panels. Detecting and assessing these shading conditions is crucial for maintaining PV system performance and longevity. This paper explores the application of machine learning (ML) techniques to identify and classify partial shading in PV systems. Six ML models are applied, namely K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), Gradient Boosting, and Adaptive Boosting (AdaBoost) Models. The study uses Wavelet Packet Transform (WPT) and Empirical Mode Decomposition (EMD) to extract Intrinsic Mode Functions (IMFs) that capture the characteristic frequencies associated with shading. These features are then used to train and validate the ML models. Among the models tested, the Random Forest model demonstrated superior performance, achieving a detection accuracy of 98.43% and a classification accuracy of 97.6%. The results highlight the effectiveness of the Random Forest model in reliably diagnosing shading faults, thereby enhancing the reliability and efficiency of PV systems. The performance of six machine-learning algorithms is validated through the cross-validation method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".