摘要
为了研究电力电缆局部放电的模式识别,解决传统单一智能算法识别率低的问题,文中提出了一种融合多深度学习算法的混合智能算法。首先,设计并制作5种典型缺陷模型以模拟实际电力电缆中的缺陷,据此展开实验并收集数据;然后,通过对PRPD谱图的相窗归一化、去极端值等改进,以及绘制PRCD谱图,更全面凸显局部放电有用特征;最后,训练基于PRPD或PRCD的多种深度学习分算法,通过可信度融合得到混合智能算法。实验结果表明,该混合智能算法相比常规单一深度学习算法识别率有显著提升,总体可达98.504%,能够准确分辨出模拟电力电缆缺陷的5种类型,具有良好应用前景。
To investigate the pattern recognition of partial discharge of power cable and address the low recognition rate of traditional single intelligent algorithm,a hybrid intelligent algorithm combining multiple deep learning algorithms is proposed in this paper.Firstly,five typical defect models are designed and made to simulate the defects in actual power cables.Subsequently,experiments were canducted and data was collected based on these models.Then,the useful features of PD were more fully highlighted by such improvement as phase window normalization and de-extremization of PRPD spectra as well as mapping of PRCD spectra.Finally,the multiple deep learning algorithms based on PRPD or PRCD were trained and a hybrid intelligent algorithm was obtained through credibility fusion.The experimental results show that compared with the conventional single deep learning algorithm,the recognition rate of the hybrid intelligent algorithm has been significantly improved,and the overall recognition rate can reach 98.504%.The hybrid intelligent algorithm can accurately distinguish 5 types of simulated power cable defects and has a good application prospect.
作者
杨朝锋
王敏
赵胜男
吴宁
董泉
崔杰
韩旭涛
YANG Chaofeng;WANG Min;ZHAO Shengnan;WU Ning;DONG Quan;CUI Jie;HAN Xutao(State Grid Henan Extra High Voltage Company,Zhengzhou 450000,China;Xi’an Jiaotong University,Xi’an 710049,China)
出处
《高压电器》
CAS
CSCD
北大核心
2023年第11期65-73,共9页
High Voltage Apparatus
关键词
局部放电
谱图特性
模式识别
深度学习
混合智能
partial discharge
spectra characteristics
pattern recognition
deep learning
hybrid intelligent
