期刊
SOLAR ENERGY
卷 190, 期 -, 页码 405-413出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2019.08.032
关键词
Fault detection; Photovoltaic (PV) systems; Machine learning; Generalized likelihood ratio test (GLRT); Gaussian process regression (GPR)
资金
- NPRP grant from Qatar National Research Fund (a member of Qatar Foundation) [NPRP9-330-2-140]
In this paper, we consider a machine learning approach merged with statistical testing hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The developed method makes use of a machine learning based Gaussian process regression (GPR) technique as a modeling framework, while a generalized likelihood ratio test (GLRT) chart is applied to detect PV system faults. The developed GPR-based GLRT approach is assessed using simulated and real PV data through monitoring the key PV system variables (current, voltage, and power). The computation time, missed detection rate (MDR), and false alarm rate (FAR) are computed to evaluate the fault detection performance of the proposed approach.
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