Research on Photovoltaic Array Compound Fault Diagnosis Based on Transfer Learning
In response to the common compound fault issues in outdoor photovoltaic arrays,a hybrid network model combining residual networks and vision transformers have been proposed and optimized using transfer learning meth-ods to enhance the reliability of fault diagnosis models in compound fault scenarios.Firstly,effective features are ex-tracted from the static I-V curves and environmental parameters of the photovoltaic arrays as inputs.Then,the model is pre-trained using simulation data.Finally,the reliability of the model in diagnosing real experimental data is veri-fied through transfer learning.The experimental results indicate that this hybrid model exhibits a higher convergence speed and accuracy when dealing with compound fault scenarios.
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维普
