基于特征融合一维卷积神经网络的电能质量扰动分类

Power quality disturbance classification of one-dimensional convolutional neural networks based on feature fusion

现有基于特征选取的电能质量扰动分类算法存在鲁棒性差、抗噪性能不强等问题。提出了一种改进的一维卷积神经网络用于电能质量扰动信号的分类。首先通过三个卷积神经网络子模型分别提取电能质量扰动信号的特征向量,然后将提取的特征向量融合为一个新的特征向量,最后通过BP神经网络实现分类。与改进前的一维卷积神经网络模型以及现有的电能质量扰动分类算法相比,该算法提取的特征向量具有更大的区分度。仿真结果表明,该算法有更好的鲁棒性和识别率,且抗噪能力强,为电能质量扰动信号分类提供了一种新思路。

The existing power quality disturbance classification algorithms based on feature selection have poor robustness and weak anti-noise performance.This paper proposes an improved one-dimensional convolutional neural network model for power quality disturbance classification.Firstly,the eigenvectors of the power quality disturbance signal are extracted by three convolutional neural network sub-models,and then the extracted eigenvector is fused into a new one;finally,the classification is implemented by BP neural network.The result shows that the proposed algorithm has higher robustness,recognition rate and strong anti-noise abilities,because the new eigenvector has greater discrimination,compared with the old one-dimensional CNN models and the existing power quality disturbance classification algorithms.It provides a new idea for classifying power quality disturbance signals.