基于数学形态学和分形理论的电缆局放识别  

Recognition of Partial Discharge of Cable Based on Mathematical Morphology and Fractal Theory

作　　者：张安安[1] 何嘉辉 李茜[1] 杨威 赵万明[1] ZHANG An-an;HE Jia-hui;LI Qian;YANG Wei;ZHAO Wan-ming(School of Electrical Engineering and Information,Southwest Petroleum University Chengdu 610500;Hangzhou Hikvision Digital Technology Co.,Ltd Hangzhou 310051)

机构地区：[1]西南石油大学电气信息学院,成都610500 [2]杭州海康威视数字技术股份有限公司,杭州310051

出　　处：《电子科技大学学报》2020年第1期102-109,共8页Journal of University of Electronic Science and Technology of China

基　　金：中国博士后基金面上项目(2014M562335);四川省教育厅科研创新团队(自然科学)项目(15TD0005)

摘　　要：为解决电缆附件绝缘缺陷故障类型识别过程复杂、特征选择冗余问题,提出了一种直接利用含噪局部放电信号进行绝缘故障识别的方法。选取3个工频周期内的含噪局放信号作为一个样本,首先采用数学形态学滤波技术进行放电脉冲提取,获得平均放电量和放电次数两个统计特征;利用Hurst指数对局放信号分形性进行判断,若条件满足,直接求取其盒维数作为一个分形特征;最后将3个特征导入可拓神经网络进行模式识别,验证该方法的可行性和有效性。结果表明:平均放电量、放电次数和盒维数3个特征具有较强的可分性,解决了特征选择冗余问题;可拓神经网络能较好地识别出不同类型的绝缘缺陷,识别率高于基于支持向量机和BP神经网络的同类方法。In order to solve the problem of complex insulation fault type recognition and redundancy of feature selection of cable accessories insulation defects, a method to recognize the fault of insulation defects by directly using the partial discharge signal containing the noise is proposed in this paper. The signals in three power frequency cycles are selected as a sample. The discharge pulse is firstly extracted by using mathematical morphological filtering technology to obtain two statistical characteristics of average discharge and discharge times. At the same time, the Hurst exponent is used to judge the fractality of partial discharge signals. If the condition is satisfied, the box dimension is directly obtained as a fractal feature. Finally, three features are imported into the extension neural network for pattern recognition to verify the feasibility and effectiveness of the method.The results show that the three features of box dimension, average discharge, and discharge times have strong separability, which can solve the problem of redundancy of feature selection, and the extension neural network can identify different types of insulation defects, and the recognition rate is better than that of similar methods based on support vector machine and BP neural network.

关 键 词：盒维数 可拓神经网络 分形理论 HURST指数 数学形态学 局部放电 

分 类 号：TM835[电气工程—高电压与绝缘技术]