Recurrence quantification analysis as a novel LC feature extraction technique for the classification of pollution severity on HV insulator model

Recurrence plot and recurrence quantification analysis are used to analyze different features of gravitational wave signals. Firstly, the appropriate delay time and embedding dimension are respectively estimated by methods of the C_C method. One dimension time series of gravitational wave is extended to high dimension phase space by employing phase space reconstruction for studying the movement characteristic of neighboring points in time series. Then the recurrence plots of different gravitational wave signals are implemented intuitively and qualitatively analyzing different features of gravitational wave signals. Different nonlinear characteristic parameters are calculated by recurrence quantification analysis (RQA) methods, such as recurrence rate, recurrence entropy, determinism rate and stratification, based on which the features of different gravitational wave signals can be well analyzed quantitatively.

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“…And a higher RR indicates a stronger cyclical embedding process. The expression for RR is as follows [11]:…”

Section: Recurrence Quantification Analysismentioning

confidence: 99%

Nonlinear analysis of gravitational wave signals based on recurrence quantification analysis

et al. 2018

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“…Besides, the number of the highest peak power-spectrum is also used as methods for pollution monitoring (Chandrasekar et al, 2009;Pylarinos et al, 2011;Pylarinos et al, 2012). Literature (Chaou et al, 2015) reported that the main frequency of the porcelain insulator LC waveform is 50, 150, and 250 Hz and the LC waveform is divided into six categories. Papers (Suda T, 2001;Bashir and Ahmad, 2010;Douar et al, 2010;Dhahbi-Megriche and Beroual, 2016) introduced the analysis of the variation characteristics of the LC in the frequency domain power spectrum during the entire process of the contamination flashover.…”

Section: Introductionmentioning

confidence: 99%

Research on Leakage Current Waveform Spectrum Characteristics of Artificial Pollution Porcelain Insulator

et al. 2021

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The surface discharge development processes of polluted porcelain insulators for power transmission lines are tightly related with the development of leakage current (LC), the characteristics of LC, the insulating condition, and discharge intensity of the insulator surface have an important significance for revealing the contamination discharge state of insulators. In order to analyze the LC characteristics of porcelain insulators in the process of pollution flashover, artificial pollution flashover tests on porcelain insulators were conducted in the artificial fog cabinet, and the characteristics of LC waveforms in time-domain and frequency-domain were simultaneously measured and analyzed during the tests. The results indicated that the amplitude of LC, fundamental harmonic, the third harmonic, and fifth harmonic had a strong correlation, the maximum of LC(Im), the rate of total harmonics (THD), and the phase difference of fundamental harmonic (θ) were used for the representation of the characteristics of the LC waveform. The LC has the characteristics of high amplitude, low proportion harmonic, and small phase difference between the fundamental harmonic and voltage before the flashover occurrence. The test results provide effective reference for porcelain insulators in pollution flashover forecasting.

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“…They are intended to perform and operate effectively under the most severe climatic conditions. Consequently, monitoring the performance of these insulators under pollution is of the upmost importance to maintain safe and continuous operation of power on the network [1][2][3][4][5][6]. If insulators are not correctly monitored especially under severe pollution conditions, the flashover can occur through the following steps: accumulation of contamination layer, wetting of the insulator, increasing of the leakage current, the formation of dry band arcs and finally the extension of such arcs to cover the leakage path [1,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this inconvenience, Chaou et al [2] employed the recurrence quantification analysis (RQA) through eight indicators to quantify and investigate LC waveforms under various pollution conductivities. The mean values of those indicators are used as inputs of three classification methods namely, K-nearest neighbours (KNNs), Naïve Bayes (NBs) and support vector machines (SVMs), to classify the contamination severity into five classes.…”

Section: Introductionmentioning

confidence: 99%

Novel colour image encoding system combined with ANN for discharges pattern recognition on polluted insulator model

Ferrah

Chaou

Maadjoudj

et al. 2020

IET Science, Measurement & Technology

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Recurrence quantification analysis as a novel LC feature extraction technique for the classification of pollution severity on HV insulator model

Cited by 27 publications

References 38 publications

Nonlinear analysis of gravitational wave signals based on recurrence quantification analysis

Nonlinear analysis of gravitational wave signals based on recurrence quantification analysis

Research on Leakage Current Waveform Spectrum Characteristics of Artificial Pollution Porcelain Insulator

Novel colour image encoding system combined with ANN for discharges pattern recognition on polluted insulator model

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