Chaotic Analysis and Feature Extraction of Vibration Signals From Power Circuit Breakers

Yang, Qiuyu; Ruan, Jiangjun; Zhuang, Zhijian; Huang, Daochun

doi:10.1109/tpwrd.2019.2934123

Cited by 32 publications

(16 citation statements)

References 26 publications

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“…It seems that the one-dimensional LC of ice-covered insulator is irregular, but its power spectrum can reflect a certain law. The power spectrum of time series with chaotic characteristics have the characteristics of continuity, wide peak and so on [29]. Taking the data in the Fig.…”

Section: A Power Spectrum Analysis Of Lcmentioning

confidence: 99%

Shape Characteristic Analysis of Chaotic Attractors for Leakage Current Signal of Ice-Covered Insulator

Hui

Lu²

2021

IEEE Access

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The change and development of leakage current (LC) during the flashover process of insulators are closely related to the discharge process. Characteristic quantities of LC play an important role in the identification and prediction of ice-covered flashover development stages. In this paper, the feature extraction method for LC of ice-covered insulator is discussed based on the shape characteristics of chaotic attractor. Firstly, the power spectrum and the largest Lyapunov exponent are used to analyze the LC of ice-covered insulator qualitatively and quantitatively, which confirmed positive chaotic features during the flashover process. On this basis, the LC is reconstructed in phase space. To solve the problem that the attractor in the high-dimensional space cannot be observed in the phase space reconstruction, the principal vector analysis method is used to project the high-dimensional reconstruction vector into the threedimensional space to reduce the dimension. Finally, the evolution law of chaotic attractor in each stage of LC under different pollution degree is analyzed and discussed. The results show that the LC of ice-covered insulator has obvious characteristics of attractor shape at different stages of pollution, and is sensitive to the degree of pollution and each stage of development. So the shape of the attractor of LC has important application value for the identification of pollution flashover state and prediction of pollution flashover process of ice-covered insulators.INDEX TERMS ice-covered insulator, leakage current, phase space reconstruction, principal vector analysis, chaotic attractor.

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Section: A Power Spectrum Analysis Of Lcmentioning

confidence: 99%

Shape Characteristic Analysis of Chaotic Attractors for Leakage Current Signal of Ice-Covered Insulator

Hui

Lu²

2021

IEEE Access

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“…For hydraulic OM, the operation process is designed in the order of 10ms [26]. Under those circumstances, the ow in hydraulic system characterizes as high pressure and large rate, which eventually affect the non-linear dynamic responses of the transmission mechanism [27].…”

Section: Introductionmentioning

confidence: 99%

A Co-simulation Model for the Hydraulic Operating Mechanism of High-Voltage Circuit Breaker

Zhao

et al. 2022

Preprint

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The dynamic characteristics of hydraulic operating mechanism is of great significance to the reliability of high-voltage circuit breaker (HVCB). Due to the lack of co-simulation models considering the coupling links of different physics, previous research works were limited to the analysis of some discrete parts. The purpose of this study is to propose a novel technique for the co-simulation of hydraulic operating mechanism (OM). For that aim, lumped parameter method is adopted for the modeling of hydraulic system and finite element analysis method is employed for that of transmission mechanism. Then, distributed parallel type co-simulation framework is applied, and the coupling links between different subsystems are achieved by input-output variables exchange with shared memory. Simulation and experimental validations are implemented in a 550KV HVCB. It proves that co-simulation model can accurately capture the dynamic responses of hydraulic OM, including predicting the dynamic responses under different operation parameters and quantifying important features like stress distribution and dynamic responses evolution.

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“…Boushaki et al proposed a multi-view fuzzy clustering algorithm based on the condensed information bottleneck audio event clustering method and representing point consistency constraints [10]. Single pass Bayesian fuzzy clustering algorithm and dynamic optimization cellular genetic fuzzy clustering method proposed by Yang et al [11]. RNA SEQ data clustering method proposed by Park and Lee [12].…”

Section: Introductionmentioning

confidence: 99%

Chaotic association feature extraction of big data clustering based on Internet of Things

Liu

Gupta

Onyema

2022

IJCAI

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This article addresses the stabilization of chaotic characteristics in abnormal data by proposing chaotic correlation feature extraction of big data clustering based on the Internet of things. The chaotic features in big data usually show complex folding and distortion without obvious rules and order and nonsynchronization. In this article, the dimension of extracted correlation is utilized as the chaotic feature for the clustering of big data. The one-dimensional time series that can be extended in multi-dimensional space is analysed based on phase space reconstruction, to extract the chaotic correlation dimension (CCD) features. After the relevant experimental analysis, this paper mainly compares the energy consumption and processing time of the two respective algorithms. In the simulation parameter design, the time interval of big data packet generation is 0.1s, and the data is generated from the simulation time of 300s. The results obtained show that when dealing with the same amount of data, the energy consumption of this algorithm is significantly lower than that of the traditional algorithm. When dealing with the same amount of data, the time required by this algorithm is significantly lower than that of the traditional algorithm. This is because this algorithm is easy to implement and has good clustering efficiency for data, so the clustering time is short. With the gradual increase in the amount of data, the correlation dimension of this algorithm tends to be stable. While the correlation dimension of the traditional algorithm fluctuates greatly, it is revealed that the proposed approach has high data clustering efficiency and verifies the effectiveness of this algorithm.Povzetek: Za internet stvari je analizirana možnost stabilizacije nenavadnih podatkov znotraj velikih podatkov.

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Chaotic Analysis and Feature Extraction of Vibration Signals From Power Circuit Breakers

Cited by 32 publications

References 26 publications

Shape Characteristic Analysis of Chaotic Attractors for Leakage Current Signal of Ice-Covered Insulator

Shape Characteristic Analysis of Chaotic Attractors for Leakage Current Signal of Ice-Covered Insulator

A Co-simulation Model for the Hydraulic Operating Mechanism of High-Voltage Circuit Breaker

Chaotic association feature extraction of big data clustering based on Internet of Things

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