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

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

doi:10.1109/tdei.2014.6740751

Cited by 7 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the phase position of the PD amplitude measurement (apparent charge) with respect to the applied voltage sinusoidal waveform. However, this approach misses information concerning the temporal evolution of PD pulses [29], [30], which can be specially important when solid or liquid dielectrics are involved [31], [32]. An alternative approach is to consider the dynamic between consecutive PD pulses, a method that Hoof and Patsch proposed and named as "Pulse Sequence Analysis" (PSA) [31].…”

Section: Introductionmentioning

confidence: 99%

“…A more general approach is to consider the PD phenomenon as a nonlinear dynamic process, and thus, to study it using the mathematical approach of nonlinear time series analysis (NLTSA) and chaos theory in order to characterize the PD behaviour and its sequence of discharges [29], [30]. NLTSA tools have been used in electrical treeing to analyze time series of PD and to describe the growth through nonlinear parameters [4], [11], [20], [35], [36].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Partial Discharges in Electrical Tree Growth Under Very Low Frequency (VLF) Excitation Through Pulse Sequence and Nonlinear Time Series Analysis

et al. 2020

View full text Add to dashboard Cite

Electrical treeing is the main degradation mechanism in high voltage polymeric insulation, that leads to power system plant failure and the loss of electricity supply. Electrical trees grow under partial discharge (PD) activity, which can be measured and analyzed to understand and characterize electrical tree growth. In this work, PD measurements were analyzed for electrical trees grown in epoxy resin needleplane samples under very low frequency (VLF, 0.1 Hz) voltage excitation. VLF is interesting as it is used for testing power cables and other high capacitance insulation loads. However, more experience and new methods are needed for PD interpretation. PDs were studied using two tools: pulse sequence analysis (PSA) and nonlinear time series analysis (NLTSA) from dynamic system theory. PSA was treated here as a particular case of NLTSA since their constructions are similar in their mathematical treatment. The experimental results showed that electrical trees grown at VLF had branch-type structure and times to breakdown about fifty times larger than samples aged at industrial frequency. PSA plots were compared with 2D projections of state-space trajectories that represent the dynamics of the nonlinear system (NLTSA approach). In terms of graphical representation, NLTSA 2D projections generated more clusters than the PSA plots, thus, it was interpreted that NLTSA revealed more details about the nonlinear dynamic system associated with electrical tree growth. On the other hand, using the NLTSA approach, the correlation dimension was estimated to characterize the electrical tree growth. The results showed a different evolution obtained for VLF excitation compared to the results reported for test samples aged at industrial frequency in other studies.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Partial Discharges in Electrical Tree Growth Under Very Low Frequency (VLF) Excitation Through Pulse Sequence and Nonlinear Time Series Analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The existence of a correlation between the nature of the PD sources and their PRPD patterns has been the motivation of designing a thoroughly automated feature extractor and pattern classifier system for the application in the area of HV insulation monitoring. In the last three decades, the automated recognition of the PD patterns has been progressively investigated and several signal processing methods and classification algorithms have been employed for the analysis of discharge patterns, such as the relative identification factor [7], time series analysis [8,9], artificial neural networks (ANNs) [4,5,[9][10][11][12][13], fuzzy algorithms [14], support vector machine (SVM) [15,16], hidden Markov models [17], pattern recognition based on the chaos theory [18], kernel statistical uncorrelated optimum discriminant vectors algorithm…”

Section: Introductionmentioning

confidence: 99%

Towards automated statistical partial discharge source classification using pattern recognition techniques

Janani

Kordi

2018

High Voltage

View full text Add to dashboard Cite

This study presents a comprehensive review of the automated classification in partial discharge (PD) source identification and probabilistic interpretation of the classification results based on the relationship between the variation of the phase-resolved PD (PRPD) patterns and the source of the PD. The proposed automated classification system consists of modern, high-performance statistical feature extraction methods and classifier algorithms. Their application in online monitoring and recognition of the PD patterns is investigated based on their low-processing time and high-performance evaluation. The application of modern statistical algorithms and pre-processing methods configured in this automated classification system improves the pattern recognition accuracy of the different PD sources that are suitable to be employed in different high-voltage (HV) insulation media. To evaluate the performance of the different combinations of the feature extraction/classier pairs, laboratory setups are designed and built that simulate various types of PDs. The test cells include three sources of PD in SF 6 , two sources of PD in transformer oil, and corona in the air. Data samples for different classes of PD sources are captured under two levels of voltage and two different levels of noise. The results of this study evaluate the suitability of the proposed classification systems for probabilistic source identification in various insulation media. Furthermore, of importance to the problem of the PD source identification is to assign a 'degree of membership' to each PRPD pattern, besides assigning a class label to it. Some of the classifier algorithms studied in this study, such as fuzzy classifiers, are not only able to show high classification accuracy rate, but they also calculate the 'degree of membership' of a sample to a class of data. This enables probabilistic interpretation of a new PRPD pattern that is being classified. The determination of the degree of membership for future PRPD samples allows safer decision making based on the risk associated with the different sources of PD in HV apparatus.

show abstract

On-line partial discharge analysis of transmission and distribution assets

Lewin

2014

2014 IEEE Electrical Insulation Conference (EIC)

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 7 publications

References 24 publications

Analysis of Partial Discharges in Electrical Tree Growth Under Very Low Frequency (VLF) Excitation Through Pulse Sequence and Nonlinear Time Series Analysis

Analysis of Partial Discharges in Electrical Tree Growth Under Very Low Frequency (VLF) Excitation Through Pulse Sequence and Nonlinear Time Series Analysis

Towards automated statistical partial discharge source classification using pattern recognition techniques

On-line partial discharge analysis of transmission and distribution assets

Contact Info

Product

Resources

About