An Adaptive Neuro-Fuzzy Model-Based Algorithm for Fault Detection in PV Systems
Bibliographic record
Abstract
This article presents an intelligent algorithm-based fault detection scheme to improve the reliability and sustainability of a photovoltaic (PV) system. The PV systems are extremely susceptible to power grid transients and their operation may suffer drastically during faults located within the solar arrays, power electronics, and the inverter. Thus, it is significantly important to develop an intelligent mechanism to detect any type of fault or abnormalities at the shortest possible time and provide security for the solar system. In order to accomplish that, an adaptive neuro-fuzzy inference system (ANFIS) is developed to distinguish between normal, and faulty operation of a grid-connected PV system. A large dataset from real-time laboratory experiment using TBD125x125-36-P PV module, which includes the current, and voltage characteristic of PV is extracted, preprocessed and used in the training of the machine learning algorithm. The performance of the proposed intelligent fault detection scheme is also compared with other popular machine learning algorithms, where ANFIS have demonstrated outstanding results, with accuracy rate of 95.4%. Furthermore, the proposed technique is significantly more robust, straightforward, and requires less implementation time compared to other machine learning techniques such as, K nearest neighbor, decision tree, Naïve Bayes, Ensemble, linear discriminant analysis, support vector machine, and finally neural network. Thus, the developed ANFIS based intelligent technique will enhance the reliability of the PV system through minimizing the maintenance cost, saving time and energy.
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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 | 0.001 |
| 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".