Intelligent health state diagnosis method for photovoltaic module

Abstract

The invention discloses an intelligent health state diagnosis method for a photovoltaic module, and the method comprises the following steps: 1, carrying out the sampling of the output I-V characteristics of a to-be-detected photovoltaic module, and carrying out the recognition of an aging parameter through an MQPSO algorithm; 2, mapping aging parameters in any working environment to STC; and 3, inputting the aging parameters into the PNN, calculating the maximum likelihood estimation value of each category, and obtaining the current health condition of the photovoltaic module to be detected. According to the method, the current health state SoH of the photovoltaic module can be reflected only by obtaining the working I-V characteristic curve of the current photovoltaic module, the environment irradiation and the environment temperature, simulation and experiment results show that the method can effectively represent the current health state of the photovoltaic module, the influence of the working environment on the health state of the module is reduced, and the method has a certain reference value for establishment of a photovoltaic module fault early warning and health management system.

Description

technical field [0001] The invention belongs to the field of diagnosis of photovoltaic components, in particular to a method for diagnosing the intelligent health status of photovoltaic components. Background technique [0002] After the photovoltaic module is installed outdoors, its electrical characteristics will decrease with the increase of use time, and the power of crystalline silicon solar cells will decrease by about 0.5% per year on average. The reason is mainly due to yellowing, corrosion, hidden cracks, cable damage, aging of solder joints, failure of bypass diodes and other phenomena in photovoltaic modules, which cause the decline of the state of health (SoH) of the modules. Although this phenomenon will not cause harm to the system for the time being, it may be an early sign of serious failure of components, thereby reducing the operating life and economic benefits of photovoltaic power plants. Therefore, testing the SoH of photovoltaic modules is an important...

AI Technical Summary

Problems solved by technology

[0004] All the above-mentioned methods have disadvantages that cannot be ignored: although infrared thermal imaging and electroluminescence technology based on field tests have high reliability, they require high experimental costs and are difficult to promote on a large scale; The time-domain detection method is susceptible to interference from environmental factors; the multi-sensor method to detect the operating parameters of the power station needs to collect a lot of data including environment, operation and maintenance, and work data, and needs a lot of equipment to assist; high-frequency signal injection and parasitic capacitance estimation Detection accuracy is low

More importantly, these methods can only evaluate changes in the health status of photovoltaic modules, and cannot quantitatively judge the current SoH of photovoltaic modules, which is difficult to apply in the health management system of photovoltaic power plants

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