Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection

Muhammad Hussain, Hussain Al-Aqrabi and Richard Hill
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Muhammad Hussain: Centre for Industrial Analytics, Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Hussain Al-Aqrabi: Centre for Industrial Analytics, Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Richard Hill: Centre for Industrial Analytics, Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK

Energies, 2022, vol. 15, issue 15, 1-14

Abstract: This paper presents a framework for photovoltaic (PV) fault detection based on statistical, supervised, and unsupervised machine learning (ML) approaches. The research is motivated by a need to develop a cost-effective solution that detects the fault types within PV systems based on a real dataset with a minimum number of input features. We discover the appropriate conditions for method selection and establish how to minimize computational demand from different ML approaches. Subsequently, the PV dataset is labeled as a result of clustering and classification. The labelled dataset is then trained using various ML models before evaluating each based on accuracy, precision, and a confusion matrix. Notably, an accuracy ranging from 94% to 100% is achieved with datasets from two different PV systems. The model robustness is affirmed by performing the approach on an additional real-world dataset that exhibits noise and missing values.

Keywords: photovoltaics; hierarchical clustering; unsupervised learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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