Abstract
In this paper, a novel fault detection and classification method for photovoltaic (PV) arrays is introduced. The method has been developed using a dataset of voltage and current measurements (I–V curves) which were collected from a small-scale PV system at the RELab, the University of Jijel (Algeria). Two different machine learning-based algorithms have been used in order to detect and classify the faults. An Internet of Things-based application has been used in order to send data to the cloud, while the machine learning codes have been run on a Raspberry Pi 4. A webpage which shows the results and informs the user about the state of the PV array has also been developed. The results show the ability and the feasibility of the developed method, which detects and classifies a number of faults and anomalies (e.g., the accumulation of dust on the PV module surface, permanent shading, the disconnection of a PV module, and the presence of a short-circuited bypass diode in a PV module) with a pretty good accuracy (98% for detection and 96% classification).
Highlights
The International Energy Agency (IEA) reports that at the end of 2020, global photovoltaic (PV) capacity installations reached 760 GWp [1]
PV monitoring systems are indispensable for the reliable operation and maintenance activities of an impressive number of photovoltaic systems
The microcontroller used in this study was not suitable for the fault classification based on the machine learning (ML) methods
Summary
The International Energy Agency (IEA) reports that at the end of 2020, global photovoltaic (PV) capacity installations reached 760 GWp [1]. PV monitoring systems are indispensable for the reliable operation and maintenance activities of an impressive number of photovoltaic systems. A fault diagnosis technique should be embedded in the system in order to prevent and isolate any possible fault, which may compromise the normal operation of a PV installation [3]. With reference to the fault diagnosis methods, a good number of machine learning (ML) methods have been developed and presented in the literature [3,4]. These have demonstrated a good ability in the detection and the classification of both common faults,
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