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    Öğe
    Fault Detection and Diagnosis Technic Using Electrical Characteristics of a PV Module and Machine Learning Classifier
    (Kırıkkale Üniversitesi, 2020) Raslan, Mouhamed Aghiad; Çam, Ertuğrul
    The growth of photovoltaic power plants is continuously rising, this growth would not be possible without safety, monitoring, and fault detection systems. In this paper, the common faults of a typical photovoltaic power plant that may occur in a photovoltaic module are discussed. Also, the paper studies the electrical characteristics of a photovoltaic module operating under several faults’ conditions applied on a specially designed module that measures the output current of each substring by utilizing sensitive Hall Effect sensors. After obtaining the electrical characteristics under faults, using machine learning, two decision trees classifier models are trained, the first classifier is trained to detect and recognize faults. However, this classifier may confuse the partial shading case with several other faults. Hence, the second decision tree classifier is trained to distinguish the exact fault type when the module is operating under partial shading condition by applying a short-circuit test on the photovoltaic module. This design can be achieved by connecting current sensors in the junction box of a typical photovoltaic module.
  • [ X ]
    Öğe
    Fault Detection and Diagnosis Technic Using Electrical Characteristics of a PV Module and Machine Learning Classifier
    (2020) Raslan, Mouhamed Aghiad; Çam, Ertuğrul
    The growth of photovoltaic power plants is continuously rising, this growth would not be possible without safety, monitoring, and fault detection systems. In this paper, the common faults of a typical photovoltaic power plant that may occur in a photovoltaic module are discussed. Also, the paper studies the electrical characteristics of a photovoltaic module operating under several faults’ conditions applied on a specially designed module that measures the output current of each substring by utilizing sensitive Hall Effect sensors. After obtaining the electrical characteristics under faults, using machine learning, two decision trees classifier models are trained, the first classifier is trained to detect and recognize faults. However, this classifier may confuse the partial shading case with several other faults. Hence, the second decision tree classifier is trained to distinguish the exact fault type when the module is operating under partial shading condition by applying a short-circuit test on the photovoltaic module. This design can be achieved by connecting current sensors in the junction box of a typical photovoltaic module.