摘要
如何有效识别高压断路器机械零部件故障严重程度是目前还未解决的问题,针对该问题,提出一种基于振动信号混沌吸引子形态特性的断路器零部件故障程度识别方法。为了更好地提取零部件早期故障的微弱故障特征,首先将振动信号按照断路器动作时序进行分时分割处理;然后提出一种参数自适应的信号分解方法,将分时振动信号各模态分量自适应地分离出来;最后重构模态分量混沌吸引子,利用混沌吸引子形态特性判断断路器零部件故障严重程度。两种不同型号断路器的试验结果表明,振动信号的模态分量混沌吸引子对故障程度具有较高的敏感度,正常与故障状态下的吸引子形态差异明显、吸引子形态随故障程度的加剧表现出一定的变化规律。该方法可为识别高压断路器机械零部件故障严重程度提供一条新思路。
How to effectively identify the fault severity for mechanical parts in high-voltage(HV)circuit breakers(CBs)is an unsolved issue so far.To address this issue,this paper proposes a fault severity identification method using morphological characteristics of chaotic attractor of CB vibration signal.First,in order to accurately extract the weak fault features for the early fault mechanical parts,the vibration signals are firstly divided into several sub-signals according to the CB’s operation sequence.Then we propose an adaptive signal decomposition method for separating the mode components from the divided sub-signals.Finally,the chaotic attractor of the mode component is reconstructed and the fault severity of mechanical part is diagnosed by the morphological characteristics of the attractor.The experimental results of two different types of CBs show that the chaotic attractor is highly sensitive to the severity of fault,and that the shape of the attractors in normal and faulty states is significantly different.The shape of the attractor varies with the aggravation of the fault severity.This method could provide a new way to identify the fault severity for mechanical parts in HVCB.
作者
杨秋玉
王栋
阮江军
翟鹏飞
Yang Qiuyu;Wang Dong;Ruan Jiangjun;Zhai Pengfei(School of Electronic,Electrical Engineering and Physics Fujian University of Technology,Fuzhou 350118 China;Electric Power Research Institute of State Grid Henan Electric Power Company,Zhengzhou 450052 China;School of Electrical Engineering and Automation Wuhan University,Wuhan 430072 China;CEE Power Co.Ltd,Fuzhou 350002 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2021年第13期2880-2892,共13页
Transactions of China Electrotechnical Society
关键词
高压断路器
故障程度
振动信号
自适应信号分解
混沌吸引子
微弱故障特征提取
High-voltage circuit breakers
fault severity
vibration signals
self-adaptive signal decomposition
chaotic attractor
weak fault feature extraction
