Classification Method for Power Quality Disturbances Based on Multi-feature Fusion Convolutional Neural Networks Combined with Transformer
With the development of renewable power generation technology,more and more renewable energy sources and equipment are applied to the power system,resulting in a significant increase in the frequency of power quality disturbances(PQDs).Accurate categorization of PQDs is essential to studying the causes and prevention of PQDs.We propose a convolutional neural network(CNN)based on multi-feature fusion combined with a Transformer model(CNN Transformer)for classifying PQDs.Fast Fourier transform(FFT)is used to extract frequency domain information from PQDs time series,and the CNN-Transformer model is used to extract features from time domain and frequency domain information of PQDs respectively to realize PQDs identification and classification.The model was used to simulate 16 types of synthesized PQDs data,and the results showed that the classification accuracy of this model is 99.88%under noiseless conditions and above 98.00%under noisy conditions,and it has good noise resistance and generalization performance.Comparison with some existing classification models further verifies that the model in this paper has the best performance among the compared models.
power qualitydisturbance classificationtime and frequency analysisconvolutional neural networkmulti-head attention mechanism
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