Photovoltaic Array Fault Diagnosis Method Based on Simulation and Random Forest Algorithms
The fault diagnosis theory based on simulation and machine learning algorithms aids in the development of fault diagnosis technology for photovoltaic power generation systems.This paper studies a photovoltaic fault diagnosis method based on Matlab simulation and the combination of the random forest algorithm.First,a simulation model of the photovoltaic power generation system is constructed using Matlab/Simulink,simulating normal operation as well as various faults such as short circuits,open circuits,aging,and shading,resulting in a large amount of simulation data as the sample set.Then,by extracting features from the training samples and training the model,a random forest fault diagnosis model is established.Finally,this fault diagnosis model is used to diagnose faults in the simulated photovoltaic system,identifying the types of faults.The proposed photovoltaic fault diagnosis method achieves an accuracy of 96.8667%on the training set and 90.2977%on the test set,providing support for the safe and reliable operation of photovoltaic power generation systems.
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