Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme

Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme

The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neu...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 3; p. 1280
Main Authors: Eltuhamy, Reham A, Rady, Mohamed, Almatrafi, Eydhah, Mahmoud, Haitham A, Ibrahim, Khaled H
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 22-01-2023
MDPI
Subjects:
adaptive neuro-fuzzy inference system
Adaptive systems
Artificial intelligence
Automation
CIGS thin film
Classification
Correlation analysis
Datasets
Dielectric films
Fault detection
Fuzzy logic
Infrared imagery
Infrared imaging
Inspection
Maintenance and repair
Neural networks
operating power ratio
Photovoltaic cells
Power plants
Principal components analysis
PV modules
Solar power plants
Thermography
Thin films
CIGS thin film
PV modules
adaptive neuro-fuzzy inference system
operating power ratio
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Summary:The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module's operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathematical parameters and I-V measurements provide a 100% classification accuracy. On the other hand, features extraction based on statistical factors provides 83.33% accuracy. A novel technique is proposed for developing a correlation matrix between the PV operating power ratio and the effective features extracted online from infrared thermal images. This eliminates the need for offline I-V measurements to estimate the operating power ratio of PV modules.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23031280