Research And Implementation Of Photovoltaic Module And Module String Fault Monitoring Algorithm

With the introduction of the concept of energy Internet,clean energy technology is developing rapidly,solar energy as a clean energy with abundant resources,wide coverage and less environmental pollution,has been widely applied all over the world.In the photovoltaic power generation array,the operation state of each photovoltaic module will affect the photovoltaic array,and even affect the operation state of other series of the confluence box.In order to ensure the safe and stable operation of photovoltaic power station,the fault monitoring scope is specific to photovoltaic modules and their clusters.This paper analyzes the operation characteristics of photovoltaic modules and their clusters,and proposes a hybrid differential evolution and particle swarm optimization algorithm-based fault monitoring method for photovoltaic modules and their clusters.This paper first studies the basic principle of solar cell power generation and the equivalent circuit of solar cell,obtains the mathematical model of solar cell,and analyzes the common topological structure of photovoltaic power station.According to the mathematical model of the solar cell has set up a simulation model for the photovoltaic modules,the output characteristics of photovoltaic components under normal condition was studied,and according to the common failure types of photovoltaic modules,for photovoltaic modules under different light intensity and temperature of output characteristic is studied,photovoltaic component is obtained by simulation models in different operation under the environment of data.Then,the particle swarm optimization algorithm and the differential evolution algorithm in the intelligent optimization algorithm are studied.Combining the characteristics of the two algorithms,the differential evolution algorithm and the particle swarm optimization algorithm are mixed.The effectiveness of the hybrid algorithm is verified by the test function.Finally,the actual operation data of the photovoltaic modules obtained from a photovoltaic power station were used to analyze the experimental data through hybrid differential evolution and particle swarm optimization algorithm,and the fault points in the photovoltaic modules were effectively searched.It was verified that this hybrid algorithm could be used for the fault monitoring of photovoltaic modules and their clusters.