On the applicability of nonlinear time series methods for partial discharge analysis

Petrov, L. A.; Lewin, P.L.; Czaszejko, Tadeusz

doi:10.1109/tdei.2013.003983

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…This necessitates data manipulation before detailed analysis, typically principle component analysis on wavelet decompositions of PD pulses, which have allowed the discrimination of distinct PD sources in noisy field data [5]. Individual PD sources can then be modelled in isolation, although it is likely that interactions between PD sources in close proximity will influence PD behaviour.…”

Section: Discussionmentioning

confidence: 99%

“…The two invariants calculated are Lempel Ziv (LZ) complexity and the Generalised Hurst Exponent (GHE), which quantify randomness and long term correlations respectively [5]. These measurements are more appropriate for investigating charge time series due to the periodicity of phase angle.…”

Section: A Experimentsmentioning

confidence: 99%

“…Although simulations show good agreement with experimental data, little work has been done on modelling PD behaviour in field conditions. Analysis of field data through non-linear measures, pulse sequence analysis and phase resolved PD patterns have shown significant differences between field and experimental data [5]. These differences, combined with the uncertainty of PD classification in field conditions, makes modelling field PD data a complex task.…”

Section: Introductionmentioning

confidence: 99%

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Physical models for field based partial discharge measurements

George

Hunter

Rapisarda

et al. 2015

2015 IEEE Electrical Insulation Conference (EIC)

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Current models of partial discharge (PD) behaviour are focused on the reproduction of deterministic, experimental data. This data is traditionally taken under strictly controlled conditions and thus exhibits minimal noise. This contrasts field PD measurements which exhibit high levels of stochasticity and extreme sensitivity to on-site conditions. In this paper partial discharge measurements are simulated from three-phase cable experiments, which aim to imitate conditions found in the field. The results show good agreement between experimental and simulated data, in terms of both phase resolved plots and non-linear invariants. The numerous additions and improvements required to describe PD behaviour in field equipment are discussed, and will form the basis for future work.

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Section: Discussionmentioning

confidence: 99%

Section: A Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physical models for field based partial discharge measurements

George

Hunter

Rapisarda

et al. 2015

2015 IEEE Electrical Insulation Conference (EIC)

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“…These surrogates are simulated time series of PD pulses that when plotted as a persistence plot will be identical to that of the original measurement data, but without the underlying relationships that are believed to exist in the real measurement data. Figure 3 shows PSA plots for a set of PD measurements made on a Central London 3 phase belted 11 kV belted cable circuit [8]. In Fig.…”

Section: A Establishing the Stochastic Nature Of On-line Pd Measuremmentioning

confidence: 99%

“…Comparison of the Hurst Exponents of the magnitude data, reveals that the artificial PD sources generate PD magnitudes that are more persistent than the field data, whereas the angle Hurst Exponent for a void discharge source is the least persistent. Other non-linear time series tools can be used to investigate the structure of field PD data and this is an on-going area of research [8].…”

Section: A Establishing the Stochastic Nature Of On-line Pd Measuremmentioning

confidence: 99%

On-line partial discharge analysis of transmission and distribution assets

Lewin

2014

2014 IEEE Electrical Insulation Conference (EIC)

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Abstract-Itis becoming increasingly clear that methodologies for PD classification based on standard laboratory experimental data are not readily applicable when assessing online PD data measured in the field. It is not just that field data is corrupted by noise and disturbance, but also the significant differences between typical laboratory experiments to generate PD data and the generation of PDs in high voltage plant due to degradation of the insulation system. In this paper, the use of nonlinear time-series analysis on field data is shown to yield useful information, methods involving dimension reduction techniques are shown to allow identification of different sources and finally a method for designing standard finite impulse response filters that approximate the nonlinear analytical approach and are easy to implement in condition monitoring systems are discussed.

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