Fault Diagnosis of Power System Based on Wavelet Transform and Artificial Intelligence
Focuses on the problem of fault diagnosis in power systems and investigates a method based on wavelet transform and artificial intelligence.Firstly,the basic framework of fault diagnosis methods for power systems was introduced,followed by a focus on time-frequency domain feature extraction based on wavelet transform and ensemble learning methods based on convolutional neural networks.In the experiment,a simulated dataset was constructed using the MATLAB platform,and the performance of traditional Convolutional Neural Network(CNN)and this method in terms of accuracy,recall,and F1 value was compared.The results show that the new method has achieved significant improvements in all indicators compared to traditional CNN,indicating that it has higher accuracy and effectiveness in power system fault diagnosis.
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