基于混沌吸引子的真空断路器永磁斥力机构机械故障识别方法

doi:10.19595/j.cnki.1000-6753.tces.210970

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摘要针对真空断路器斥力机构故障时振动信号瞬态、非线性且难以进行故障定量评估与识别的问题,该文提出一种基于吸引子形态的混沌吸引子矩特征量和粒子群优化支持向量机的斥力机构故障评估与识别方法。以斥力机构分合闸过程中典型故障,特别是分闸油压缓冲器不同过调故障程度的振动信号为分析对象,首先采用混沌理论相空间重构方法和Wolf算法,得到最大Lyapunov指数,表明斥力机构振动信号具有明显的混沌特性,通过三维相图定性分析振动信号混沌吸引子演变规律;然后引入混沌吸引子矩理论,提取四种二维吸引子矩,利用最小二乘法线性回归计算第一区域曲线斜率为吸引子形态特征量,同时提取时域信号共同组成特征向量库;最后比对分析支持向量机算法与粒子群优化-支持向量机组合算法的准确率。实验结果表明,文中所用方法可精准识别斥力机构故障类型。

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关键词 ：真空断路器, 斥力机构, 油压缓冲器, 吸引子形态, 支持向量机

Abstract：In order to solve the problem that the vibration signal of repulsive mechanism of vacuum circuit breaker is transient, nonlinear, and it is difficult to quantitatively evaluate and identify the fault, this paper proposes a method of repulsive mechanism fault evaluation and identification based on chaotic attractor moment feature of attractor morphology and particle swarm optimization support vector machine. The typical faults of the repulsive mechanism in the process of opening and closing, especially the vibration signals of the opening oil buffer with different degrees of overmodulation fault, are analyzed. Firstly, the phase space reconstruction method of chaos theory and Wolf algorithm are used to obtain the maximum Lyapunov exponent, which shows that the vibration signal of repulsive mechanism has obvious chaotic characteristics. The evolution of chaotic attractor of the vibration signal is qualitatively analyzed using three-dimensional phase diagram. Then, the chaotic attractor moment theory is introduced to extract four kinds of two-dimensional attractor moments. The slope of the first region curve is calculated as the attractor morphological feature by the least square linear regression method, and the time-domain signals are extracted to form the feature vector library. Finally, the accuracy of the support vector machine algorithm and the particle swarm optimization-support vector machine combination algorithm is compared. The experimental results show that the proposed method can accurately identify the fault types of repulsive mechanism.

Key words： Vacuum circuit breaker repulsion mechanism oil buffer attractor morphology support vector machine

收稿日期: 2021-07-02

PACS:

TM561

基金资助:中央引导地方科技发展专项（20ZYJDJC00060）和河北省自然科学基金面上项目（E2021202186）资助

通讯作者: 刘晓明女,1968年生,教授,博士生导师,研究方向为现代电器设计与应用、高电压与绝缘技术、智能电器。E-mail: liuxiaoming@hebut.edu.cn

作者简介: 张煦松男,1996年生,硕士研究生,研究方向为现代电器设计与应用、电器设备状态检测、智能电器、混沌分析。E-mail: 1508189330@qq.com

引用本文:

刘晓明, 张煦松, 姜文涛, 陈海, 陈军平, 宋柏阳. 基于混沌吸引子的真空断路器永磁斥力机构机械故障识别方法[J]. 电工技术学报, 2022, 37(20): 5334-5346. Liu Xiaoming, Zhang Xusong, Jiang Wentao, Chen Hai, Chen Junping, Song Boyang. A Method of Mechanical Fault Identification of Permanent Magnet Repulsion Mechanism of Vacuum Circuit Breaker Based on Chaos Attractor. Transactions of China Electrotechnical Society, 2022, 37(20): 5334-5346.

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https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.210970 https://dgjsxb.ces-transaction.com/CN/Y2022/V37/I20/5334

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