Abstract
With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN.
Highlights
With the development of renewable energy, solar photovoltaic (PV) has become one of the key development projects [1]
Photovoltaic systems are exposed for a long time to outdoor environments with a high temperature and high humidity, which will cause the system performance, reliability, optoelectronic and material properties of the PV module to degrade and become faulty over time [2,3,4]
In ref. [2], the electrical aging of three types of crystalline silicon PV modules with different cells, encapsulates and back-sheets are proposed for accelerating during extended damp-heat tests at different stress levels
Summary
With the development of renewable energy, solar photovoltaic (PV) has become one of the key development projects [1]. Photovoltaic systems are exposed for a long time to outdoor environments with a high temperature and high humidity, which will cause the system performance, reliability, optoelectronic and material properties of the PV module to degrade and become faulty over time [2,3,4]. [2], the electrical aging of three types of crystalline silicon PV modules with different cells, encapsulates and back-sheets are proposed for accelerating during extended damp-heat tests at different stress levels. The humidity dosage is utilized to evaluate the degradation of power and the equivalent changes of solar cell circuit parameters. The PV modules were tested in a relatively high humidity and damp-heat (DH) stress-testing chamber, in an accelerated condition at 85%
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