北华大学学报（自然科学版）2025，Vol.26Issue(1)：115-124,10.DOI:10.11713/j.issn.1009-4822.2025.01.019
基于多特征融合卷积神经网络结合Transformer的电能质量扰动分类方法
Classification Method for Power Quality Disturbances Based on Multi-feature Fusion Convolutional Neural Networks Combined with Transformer
摘要
Abstract
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.关键词
电能质量/扰动分类/时频分析/卷积神经网络/多头注意力机制Key words
power quality/disturbance classification/time and frequency analysis/convolutional neural network/multi-head attention mechanism分类
动力与电气工程引用本文复制引用
王高峰,张卓石,高蔓,钱云..基于多特征融合卷积神经网络结合Transformer的电能质量扰动分类方法[J].北华大学学报（自然科学版）,2025,26(1):115-124,10.基金项目
吉林省科技发展计划项目(20210203169SF). （20210203169SF）
