Advanced Non-Invasive Health Monitoring for Solar PV Panels Using an Enhanced Ensemble Classifier Approach

Title

Advanced Non-Invasive Health Monitoring for Solar PV Panels Using an Enhanced Ensemble Classifier Approach

Author

1. Swapnil Sambhus, Student, Universitas Indonesia, Indonesia

Abstract

Maintaining the efficiency of solar photovoltaic (PV) systems is crucial for optimal energy production. Traditional invasive methods for diagnosing PV panel health are labor-intensive and time-consuming. This paper presents an advanced, non-invasive diagnostic approach that uses an enhanced ensemble classifier to identify faults, degradation, and performance issues in solar PV panels. By leveraging multiple machine learning models, the ensemble classifier accurately assesses the condition of panels based on non-invasive input parameters like voltage, current, and temperature. Simulation results validate the improved accuracy and robustness of the proposed method over individual classifiers.

Keywords

Conclusion

This research introduced an ensemble classifier-based non-invasive health diagnostic system for photovoltaic (PV) panels. Reliable defect identification without physical intervention is made possible by the proposed model, which incorporates different machine-learning approaches to boost diagnostic accuracy. Results from both simulations and experiments show that the ensemble classifier outperforms conventional single classifiers regarding accuracy and resilience.

A wider variety of climatic circumstances should be included in the dataset in future studies, and the system's real-time deployment in solar power plants should be investigated. Further investigation into the possibility of combining the model with IoT technologies for ongoing monitoring will also be undertaken.

Author Contrubution

The study's design, data collection, result analysis, and manuscript preparation were entirely managed by the author.

Funding

No grants from public, commercial, or non-profit funding agencies supported the research, authorship, or publication of this article.

Conflict of Interest

The authors disclose no conflicts of interest in relation to this work.

Data Sharing Statement

ata sharing is not part of this study.

Software And Tools Use

The research did not involve the use of any particular software or tools.

Acknowledgements

I appreciate the assistance and expertise provided by everyone involved in this research and manuscript, and the valuable comments from peer reviewers.