Skip to main content
SpringerLink
Log in

Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning classification for different types of experimental electrode systems. A polyethylene sheet was used as the sample, and PD was generated by applying a high AC voltage to four different types of electrodes. The number of PD pulses was counted from the raw data as preprocessing to calculate the feature value for the machine learning. The PD generation rate was defined for each phase angle section. Four types of machine learning algorithms were adopted for the classification of the electrode system: k-NN (k-nearest neighbor algorithm), logistic regression, decision tree, and random forest. The best accuracy was obtained by using the random forest algorithm (0.97), and it was found that k-NN also demonstrated good performance. The parameter dependencies were also evaluated for each algorithm. Based on the results generated by the random forest, it became clear that there were phase angle sections that were of high importance. The reason for the result was discussed from the perspective of (i) the difference due to the electrical circuit parameters (modified abc-model) and (ii) the stochastic fluctuation of PD signal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

source voltage (V0)

Fig. 15

Similar content being viewed by others

References

  1. Bartnikas R (2002) Partial discharges their mechanism, detection and measurement. IEEE Trans Dielectr Electr Insul 9:763–808. https://doi.org/10.1109/TDEI.2002.1038663

    Article  Google Scholar 

  2. Callender G, Lewin PL (2020) Modeling partial discharge phenomena. IEEE Electr Insul Mag 36:29–36. https://doi.org/10.1109/MEI.2020.9070114

    Article  Google Scholar 

  3. Niemeyer L (1995) A generalized approach to partial discharge modeling. IEEE Trans Dielectr Electr Insul 2:510–528. https://doi.org/10.1109/94.407017

    Article  Google Scholar 

  4. Crichton GC, Karlsson PW, Pedersen A (1989) Partial discharges in ellipsoidal and spheroidal voids. IEEE Trans Electr Insul 24:335–342. https://doi.org/10.1109/14.90292

    Article  Google Scholar 

  5. Boggs SA (1990) Partial discharge. III. Cavity-induced PD in solid dielectrics. IEEE Electr Insul Mag 6:11–16. https://doi.org/10.1109/57.63094

    Article  Google Scholar 

  6. McAllister IW, Crichton GC (2005) Influence of bulk dielectric polarization upon partial discharge transients effect of heterogeneous dielectric geometry. IEEE Trans Dielectr Electr Insul 7:124–132. https://doi.org/10.1109/94.839350

    Article  Google Scholar 

  7. Illias H, Chen G, Lewin PL (2010) Modeling of partial discharge activity in spherical cavities within a dielectric material. IEEE Electr Insul Mag 27:38–45. https://doi.org/10.1109/MEI.2011.5699446

    Article  Google Scholar 

  8. Gouda OE, ElFarskoury AA, Elsinnary AR, Farag AA (2018) Investigating the effect of cavity size within medium-voltage power cable on partial discharge behaviour. IET Gener Transm Distrib 12:1190–1197. https://doi.org/10.1049/iet-gtd.2017.1012

    Article  Google Scholar 

  9. Maekawa H, Doi M, Kawamoto S (2001) Equivalent circuit model of partial discharge for needle on spacer in gas insulated switchgegrs. IEEJ Trans Power Energy 121:1169–1174. https://doi.org/10.1541/ieejpes1990.121.9_1169

    Article  Google Scholar 

  10. Patsch R, Berton F (2002) Pulse sequence analysis: a diagnostic tool based on the physics behind partial discharges. J Phys D Appl Phys 35:25–32. https://doi.org/10.1088/0022-3727/35/1/306

    Article  Google Scholar 

  11. Achillides Z, Georghiou GE, Kyriakides E (2008) Partial discharges and associated transients: the induced charge concept versus capacitive modeling. IEEE Trans Dielectr Electr Insul 15:1507–1516. https://doi.org/10.1109/TDEI.2008.4712652

    Article  Google Scholar 

  12. Achillides Z, Danikas MG, Kyriakides E (2017) Partial discharge modeling and induced charge concept: comments and criticism of pedersen’s model and associated measured transients. IEEE Trans Dielectr Electr Insul 24:1118–1122. https://doi.org/10.1109/TDEI.2017.006013

    Article  Google Scholar 

  13. Lemke E (2012) A critical review of partial-discharge models. IEEE Electr Insul Mag 28:11–16. https://doi.org/10.1109/MEI.2012.6340519

    Article  Google Scholar 

  14. Hikita M, Yamada K, Nakamura A, Mizutani T, Oohasi A, Ieda M (1990) Measurements of partial discharges by computer and analysis of partial discharge distribution by the Monte Carlo method. IEEE Trans Electr Insul 25:453–468. https://doi.org/10.1109/14.55716

    Article  Google Scholar 

  15. Heitz C (1999) A generalized model for partial discharge processes based on a stochastic process approach. J Phys D Appl Phys 32:1012–1023. https://doi.org/10.1088/0022-3727/32/9/312

    Article  Google Scholar 

  16. Ovsyannikov AG, Korobeynikov SM, Vagin DV (2017) Simulation of apparent and true charges of partial discharges. IEEE Trans Dielectr Electr Insul 24:3687–3693. https://doi.org/10.1109/TDEI.2017.006635

    Article  Google Scholar 

  17. Trotsenko Y, Brzhezitsky V, Masluchenko I (2017) Circuit simulation of electrical breakdown in air using Kind’s equal-area criterion. Technol Audit Prod Reserv 35:44–49. https://doi.org/10.15587/2312-8372.2017.102240

    Article  Google Scholar 

  18. Karpov DI, Kupershtokh AL, Meredova MB, Zuev MV (2017) Simulation of partial discharges in cavities and streamers with high spatial resolution. J Phys: Conf Ser 899:082001. https://doi.org/10.1088/1742-6596/899/8/082001

    Article  Google Scholar 

  19. Trotsenko Y, Brzhezitsky V, Protsenko O, Chumack V, Haran Y (2018) Simulation of partial discharges under influence of impulse voltage. Technology Audit and Production Reserves 39:36–41. https://doi.org/10.15587/2312-8372.2018.123309

    Article  Google Scholar 

  20. Whitehead S (1951) Dielectric breakdown in solids. Clarendon Press, Oxford

    MATH  Google Scholar 

  21. Okamoto T, Tanaka T (1985) Analysis of partial discharge characteristics by an equivalent circuit with fluctuating discharge time lag. The Trans Inst Electr Eng Jpn 105:269–275. https://doi.org/10.1541/ieejfms1972.105.269

    Article  Google Scholar 

  22. Mukherjee S, Chattopadhyay AB, Thomas S (2018) Electrostatic field theoretic approach to analyze the partial discharge phenomenon pertaining to insulation degradation. Int J Eng Technol 7:842–848. https://doi.org/10.14419/ijet.v7i2.12095

    Article  Google Scholar 

  23. Trotsenko Y, Brzhezitsky V, Protsenko O, Mykhailenko V (2019) Application of three-capacitance models for simulation of partial discharges in solid dielectric containing several cavities. In: 2019 IEEE 2nd Ukraine conference on electrical and computer engineering, pp 279–282. DOI: https://doi.org/10.1109/UKRCON.2019.8879931

  24. Hikita M, Kato T, Okubo H (1994) Partial discharge measurements in SF/sub 6/ and air using phase-resolved pulse-height analysis. IEEE Trans Dielectr Electr Insul 1:276–283. https://doi.org/10.1109/94.300260

    Article  Google Scholar 

  25. Suwarno SY, Komori F, Mizutani T (1996) Partial discharges due to electrical treeing in polymers: phase-resolved and time-sequence observation and analysis. J Phys D Appl Phys 29:2922–2931. https://doi.org/10.1088/0022-3727/29/11/028

    Article  Google Scholar 

  26. Altenburger R, Heitz C, Timmer J (2002) Analysis of phase-resolved partial discharge patterns of voids based on a stochastic process approach. J Phys D Appl Phys 35:1149–1163. https://doi.org/10.1088/0022-3727/35/11/309

    Article  Google Scholar 

  27. Hudon C, Belec M (2005) Partial discharge signal interpretation for generator diagnostics. IEEE Trans Dielectr Electr Insul 12:297–319. https://doi.org/10.1109/TDEI.2005.1430399

    Article  Google Scholar 

  28. Das S, Purkait P (2008) ϕ-q-n pattern analysis for understanding partial discharge phenomena in narrow voids. In: 2008 IEEE power and energy society general meeting - conversion and delivery of electrical energy in the 21st century, pp 1–7. https://doi.org/10.1109/PES.2008.4596196.

  29. Liao R, Yan J, Yang L, Zhu M, Liu B (2011) Study on the relationship between damage of oil-impregnated insulation paper and evolution of phase-resolved partial discharge patterns. Eur Trans Electr Power 21:2112–2124. https://doi.org/10.1002/etep.546

    Article  Google Scholar 

  30. Kasinathan E, Mahajan A, Gupta N (2017) Phase resolved PD patterns in treeing in the presence of voids. J Electrostat 87:45–50. https://doi.org/10.1016/j.elstat.2017.03.004

    Article  Google Scholar 

  31. Kunicki M, Cichon A (2018) Application of a phase resolved partial discharge pattern analysis for acoustic emission method in high voltage insulation systems diagnostics. Arch Acoust 43:235–243. https://doi.org/10.24425/122371

    Article  Google Scholar 

  32. Yanagizawa T, Iwamoto S, Okamoto T, Fukagawa H (1991) Recognition of the partial discharge pattern of the electrode voids by neural network theory. IEEJ Trans Power Energy 111:706–712. https://doi.org/10.1541/ieejpes1990.111.7_706

    Article  Google Scholar 

  33. Okamoto T (1995) Recent partial discharge diagnostics. IEEJ Trans Power Energy 115:1136–1139. https://doi.org/10.1541/ieejpes1990.115.10_1136

    Article  Google Scholar 

  34. Hara T, Hiraiwa N, Hirotsu K, Fukunage S (1996) Detection of partial discharges in power cables by neural network. EEJ Trans Electron Inf Syst 116:734–740. https://doi.org/10.1541/ieejeiss1987.116.7_734

    Article  Google Scholar 

  35. Catterson VM, Sheng B (2015) Deep neural networks for understanding and diagnosing partial discharge data. IEEE Electr Insul Conf 2015:218–221. https://doi.org/10.1109/ICACACT.2014.7223616

    Article  Google Scholar 

  36. Li G, Rong M, Wang X, Li X, Li Y (2016) Partial discharge patterns recognition with deep convolutional neural networks. Int Conf Cond Monit Diagn 2016:324–327. https://doi.org/10.1109/CMD.2016.7757816

    Article  Google Scholar 

  37. Li G, Wang X, Li X, Yang A, Rong M (2018) Partial discharge recognition with a multi-resolution convolutional neural network. Sensors 18:3512. https://doi.org/10.3390/s18103512

    Article  Google Scholar 

  38. Zhang Q, Lin J, Song H, Sheng G (2018) Fault identification based on PD ultrasonic signal using RNN, DNN and CNN. Cond Monit Diagn 2018:1–6. https://doi.org/10.1109/CMD.2018.8535878

    Article  Google Scholar 

  39. Wang X, Huang H, Hu Y, Yang Y (2018) Partial discharge pattern recognition with data augmentation based on generative adversarial networks. Cond Monit Diagn 2018:1–4. https://doi.org/10.1109/CMD.2018.8535718

    Article  Google Scholar 

  40. Ardila-Rey JA, Ortiz JE, Creixell W, Muhammad-Sukki F, Bani NA (2020) Artificial generation of partial discharge sources through an algorithm based on deep convolutional generative adversarial networks. IEEE Access 8:24561–24575. https://doi.org/10.1109/ACCESS.2020.2971319

    Article  Google Scholar 

  41. Zemouri R, Lévesque M, Amyot N, Hudon C, Kokoko O, Tahan SA (2020) Deep convolutional variational autoencoder as a 2D-visualization tool for partial discharge source classification in hydrogenerators. IEEE Access 8:5438–5454. https://doi.org/10.1109/ACCESS.2019.2962775

    Article  Google Scholar 

  42. Morette N, Ditchi T, Oussar Y (2020) Feature extraction and ageing state recognition using partial discharges in cables under HVDC. Electric Power Syst Res 178:106053. https://doi.org/10.1016/j.epsr.2019.106053

    Article  Google Scholar 

  43. Adam B, Tenbohlen S (2021) Classification of superimposed partial discharge patterns. Energies 14:2144. https://doi.org/10.3390/en14082144

    Article  Google Scholar 

  44. Fixt E, Hodges JL Jr (1989) Discriminatory analysis-nonparametric discrimination: consistency properties. Int Stat Rev 57:238–247. https://doi.org/10.2307/1403797

    Article  MATH  Google Scholar 

  45. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat. https://doi.org/10.2307/2685209

    Article  MathSciNet  Google Scholar 

  46. Zou X, Hu Y, Tian Z, Shen K (2019) Logistic regression model optimization and case analysis. In: 2019 IEEE 7th international conference on computer science and network technology, pp 135–139. https://doi.org/10.1109/ICCSNT47585.2019.8962457

  47. Qin J, Lou Y (2019) L1–2 regularized logistic regression. In: 2019 53rd Asilomar conference on signals, systems, and computers, pp 779–783. https://doi.org/10.1109/IEEECONF44664.2019.9048830

  48. Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol 1, pp 278–282. https://doi.org/10.1109/ICDAR.1995.598994

  49. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844. https://doi.org/10.1109/34.709601

    Article  Google Scholar 

  50. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  51. Agoris DP, Hatziargyriou ND (1993) Approach to partial discharge development in closely coupled cavities embedded in solid dielectrics by the lumped capacitance model. IEE Proc A Sci Meas Technol 2:131–134. https://doi.org/10.1049/ip-a-3.1993.0021

    Article  Google Scholar 

  52. Okamoto T, Kato T, Yokomizu Y, Suzuoki Y (2000) Integral equation represention for partial discharge fluctuation. IEEJ Trans Fundam Mater 120:490–498. https://doi.org/10.1541/ieejfms1990.120.4_490

    Article  Google Scholar 

  53. Okamoto T, Kato T, Yokomizu Y, Suzuoki Y (2000) Fluctuation analysis of partial discharge pulse occurrence with an integral equation. IEEJ Trans Fundam Mater 120:620–630. https://doi.org/10.1541/ieejfms1990.120.5_620

    Article  Google Scholar 

  54. Nozu D, Okamoto T (2016) Integral equation for analysis of partial discharge pulses based on residual voltage fluctuation. IEEJ Trans Fundam Mater 136:812–816. https://doi.org/10.1541/ieejfms.136.812

    Article  Google Scholar 

  55. Nozu D, Okamoto T (2017) Integral equation for analysis of partial discharge pulses under sinusoidal voltage stress. IEEJ Trans Fundam Mater 137:529–535. https://doi.org/10.1541/ieejfms.137.529

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Prof. H. Uehara and Dr. T. Okamoto for the fruitful discussions.

Funding

This work was partially supported by the Japan Society for the Promotion of Science KAKENHI (Grant Number JP20H02140).

Author information

Authors and Affiliations

  1. Research Division of Product Reliability, Osaka Research Institute of Industrial Science and Technology, 7-1, Ayumino-2, Izumi-shi, Osaka, 5941157, Japan

    Shinya Iwata & Ryota Kitani

Authors
  1. Shinya Iwata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryota Kitani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shinya Iwata.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Availability of data and material

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iwata, S., Kitani, R. Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification. Electr Eng 103, 3189–3199 (2021). https://doi.org/10.1007/s00202-021-01306-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-021-01306-5

Keywords

Search

Navigation