Decision tree-based fault detection and classification in solar photovoltaic arrays

Because of the non-linear output characteristics of PV arrays, a variety of faults may be difficult to detect by conventional protection devices. To detect and classify these unnoticed faults, a fault detection and classification method has been proposed based on decision trees (DT). Readily availab...

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Bibliographic Details
Main Authors: Ye Zhao, Ling Yang, Lehman, B., de Palma, J., Mosesian, J., Lyons, R.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Arrays
Circuit faults
Data models
Fault detection
Training
Voltage measurement
Citations: Items that cite this one
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Description
Summary:Because of the non-linear output characteristics of PV arrays, a variety of faults may be difficult to detect by conventional protection devices. To detect and classify these unnoticed faults, a fault detection and classification method has been proposed based on decision trees (DT). Readily available measurements in existing PV systems, such as PV array voltage, current, operating temperature and irradiance, are used as "attributes" in the training and test set. In experimental results, the trained DT models have shown high accuracy of fault detection and fault classification on the test set.
ISSN:1048-2334
2470-6647
DOI:10.1109/APEC.2012.6165803