基于改进CWD-CNN的配电网内部过电压类型识别方法  

Recognition method of internal overvoltage type for distribution network via improved CWD-CNN

作　　者：高伟[1] 郭谋发[1] 许立彬 GAO Wei;GUO Mou-fa;XU Li-bin(College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China;State Grid Fujian Maintenance Company,Fuzhou 350001,China)

机构地区：[1]福州大学电气工程与自动化学院,福州350108 [2]国网福建省电力有限公司检修分公司,福州350001

出　　处：《电机与控制学报》2020年第8期131-140,共10页Electric Machines and Control

基　　金：国家自然科学基金(51677030);晋江市福大科教园区发展中心科研项目(2019-JJFDKY-23)。

摘　　要：针对配电网内部过电压类别难以辨识的问题,提出了基于改进CWD-CNN的过电压类型识别方法。采用乔威廉姆斯分布(choi-williams distribution,CWD)对电力系统中常见的7种过电压信号进行时频分解,构造可表达过电压信号时频能量特征的二维矩阵,并利用卷积神经网络(convolutional neural network,CNN)进行过电压的分类识别。改进后的CNN卷积核具有长方形尺度,能够高效、迅速地对时频图像进行特征提取。同时,分别从迭代次数、批量样本数、隐层特征图个数以及卷积核尺寸等方面分析其对寻优结果的影响,并确定最佳寻优参数,最后从样本库随机抽取数据进行交叉验证。结果表明,该方法能够有效地对7类过电压信号进行分类识别,并具有较高的识别率,避免了人工提取特征量的局限性和复杂性。A recognition method of internal overvoltage in distribution network via improved CWD-CNN is proposed in order to solve the problem that the overvoltage category is difficult to identify. By applying Choi-Williams distribution( CWD) on seven common overvoltage signals,two-dimensional matrix for timefrequency energy characteristic of overvoltage signals was constructed. Then the classification of overvoltage was carried out by means of convolutional neural network( CNN). The convolution kernel of the improved CNN has a rectangular scale,and it can extract the features of time-frequency images efficiently and quickly. The impact of the number of iterations,the number of samples,the number of hidden layers,the size of the convolution kernel,on the optimization results were analyzed to determine the optimum parameters. Finally,cross-validation was performed by random sampling of data from the sample base. The results show that the method can effectively classify the seven kinds of overvoltage signals,and it has a higher recognition. The proposed method avoids the limitation and complexity of extracting feature quantity manually.

关 键 词：内部过电压 乔威廉姆斯分布 时频能量特征 卷积神经网络 长方形卷积核 参数寻优 

分 类 号：TM73[电气工程—电力系统及自动化]