Photovoltaic module fault diagnosis method, system and device based on deep convolutional adversarial network

Abstract

The invention provides a photovoltaic module fault diagnosis method of a deep convolution generative adversarial network. The method comprises the steps of establishing a mathematical model of a photovoltaic module; carrying out fault image acquisition on the photovoltaic module; setting a part of fault data as a training sample; constructing a training model of the deep convolutional adversarialnetwork; the generator G inputting a noise vector and outputting a pseudo image through a deconvolution layer; the discriminator D inputting a real sample and a pseudo sample, extracting convolution features through convolution operation, and obtaining the probability of the real sample; optimizing a weight parameter through a back propagation algorithm, then starting the next cycle, and outputting a test image every 300 cycles; and inputting the real sample and the obtained test sample into a classifier to classify fault types, thereby realizing fault diagnosis. According to the fault diagnosis method, a large number of fault pictures are generated by using the deep convolutional network, and a fault image database is expanded, so that fault classification is more detailed, and fault diagnosis is more accurate.

Description

technical field [0001] The invention relates to a method for diagnosing faults of photovoltaic components, in particular to a method for diagnosing faults of photovoltaic components based on a deep convolutional confrontation network. Background technique [0002] Photovoltaic, as one of the new energy sources, has attracted widespread attention. In recent years, photovoltaic power generation has developed rapidly. Photovoltaic modules are an important component of photovoltaic power generation systems, and the research on fault diagnosis of photovoltaic modules is particularly important. Photovoltaic power stations are generally built in open areas such as high places and open fields. In harsh environments, photovoltaic modules are prone to various failures, so daily monitoring and equipment maintenance are very important. However, the traditional manual inspection is time-consuming and labor-intensive, and it is also dangerous, which can no longer meet the increasingly la...

AI Technical Summary

Problems solved by technology

However, the traditional manual inspection is time-consuming and labor-intensive, and it is also dangerous, which cannot satisfy the increasingly large photovoltaic power generation system.

Fault diagnosis methods for photovoltaic modules in the prior art, such as the most common I-V curve method, can detect simple fault types, but are often not accurate enough for complex faults; there are also mathematical model methods, which are more complicated due to the variety of photovoltaic faults , so it is difficult to establish an accurate mathematical model; and infrared detection technology is greatly affected by the environment and climate, and the collected fault data samples often have low resolution and large noise

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