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首页> 外文期刊>IEEE transactions on industrial informatics >An Automatic Identification Framework for Complex Power Quality Disturbances Based on Multifusion Convolutional Neural Network
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An Automatic Identification Framework for Complex Power Quality Disturbances Based on Multifusion Convolutional Neural Network

机译：基于多化卷积神经网络的复杂电能质量障碍自动识别框架

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摘要

Intelligent identification of multiple power quality (PQ) disturbances is very useful for pollution control of power systems. In this paper, we propose a novel detection framework for complex PQ disturbances based on multifusion convolutional neural network (MFCNN). Our contributions focus on automatic extraction and fusion of features from multiple sources. First, an information fusion structure is introduced in which the time domain and frequency domain information of the PQ disturbance signal are used as inputs. Additionally, the one-dimensional composite convolution is proposed to improve the diversity of network features based on the standard convolution and dilated convolution. Then, to speed up the training and prevent overfitting, batch normalization is used to adjust the distribution of features. Second, we use several visualization methods to resolve the internal mode of MFCNN, and demonstrate the working mechanism of the proposed method. Finally, we conduct various experiments to verify the effectiveness of the MFCNN. Compared with the handcrafted feature design methods and the general convolutional neural network models, the simulation under different noises and hardware platform-based experiments verify the effectiveness of noise immunity, higher training speed, and better accuracy of the method.
机译：智能识别多功能质量（PQ）干扰对于电力系统的污染控制非常有用。本文提出了一种基于多化卷积神经网络（MFCNN）的复杂PQ扰动的新型检测框架。我们的贡献专注于多种来源的自动提取和融合功能。首先，引入了信息融合结构，其中PQ扰动信号的时域和频域信息用作输入。另外，提出了一维复合卷积，以改善基于标准卷积和扩张卷积的网络特征的多样性。然后，为了加快培训并防止过度拟合，批量归一化用于调整特征的分布。其次，我们使用多种可视化方法来解决MFCNN的内部模式，并演示所提出的方法的工作机制。最后，我们进行各种实验来验证MFCNN的有效性。与手工制作的特征设计方法和一般的卷积神经网络模型相比，在不同噪声和基于硬件平台的实验下的仿真验证了抗噪性，训练速度较高，更好的方法的有效性。

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