Digital Twin-Based Kernel RF for Fault Detection and Diagnosis in Photovoltaic Systems
Bibliographic record
Abstract
Ensuring the reliability, efficiency, and economic viability of photovoltaic (PV) systems requires effective fault detection and diagnosis, which remains a significant challenge. In this work, we propose a novel and integrated framework that leverages Digital Twin (DT) technology together with the RK-RF algorithm for fault detection and classification in grid-connected PV systems. The DT serves as a dynamic reference model, capturing real-time system behavior and generating sensitive error indicators for anomaly detection. These indicators are then efficiently processed by the RK-RF algorithm, which incorporates Kernel Principal Component Analysis (KPCA) for dimensionality reduction, enhancing classification accuracy while reducing computational costs compared to traditional machine learning or deep learning methods. Experimental validation on a grid-connected PV system emulator, including one healthy state and five representative fault scenarios, demonstrates the high effectiveness of the proposed method. Specifically, the DT enables rapid and reliable anomaly detection, while the RK-RF achieves near-perfect classification accuracy (≈ 100%), outperforming benchmark techniques such as support vector machines (SVM), k-nearest neighbors (KNN), neural networks (NN), and recurrent neural networks (RNN). Overall, the proposed DT-RK-RF framework provides a robust, scalable, and interpretable solution for predictive maintenance and performance optimization in PV systems, with potential applicability to other complex energy and industrial infrastructures. This work emphasizes both computational efficiency and real-time reliability, addressing key limitations of existing DT+AI approaches.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".