Abstract
Regular maintenance of power equipment in high voltage power systems is essential for avoiding outages. An effective way to maintain such systems is the measurement of partial discharges in the insulation material. Voids in solid dielectrics may result from many causes including defects taking place during the manufacturing of the dielectric. These voids induce PDs. Classifying different void sizes is challenging since traditional classification tools used for partial discharge (PD) classification do not work properly. For instance, phase resolved partial discharge (PRPD) patterns resulting from different void sizes will be roughly the same since the source of the partial discharge is the same. Using existing clustering techniques such as Time–Frequency (T–F) map or analysis of statistical features extracted from the PRPD patterns presents their own limitations. T–F map restricts the use of Fast Fourier Transform, while working with PRPDs is only applicable for AC measurements. In this paper, a convolutional neural network (CNN) attention-based model has shown superior capability over traditional classification technique (T–F map) to classify partial discharge (PD) waveforms resulting from different voids in PLA 3D-printed samples. 1D-CNN has classification accuracy of 98.7% with an increase of 21.42% compared to the T–F map technique. Extensive investigation of the learned model has been conducted in order to interpret the decisions made by the proposed neural network. In particular, adding an interpretable attention model such as GRAD-CAM to our CNN shows that while making the decision the neural network learns to focus more on the regions of the waveform corresponding to the rise of the pulse.
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References
Satish L, Zaengl WS (1994) Artificial neural networks for recognition of 3-d partial discharge patterns. IEEE Trans Dielectr Electr Insul 1(2):265–275
Whitehead S (1951) Dielectric breakdown of solids. Clarendon Press, London
Crichton GC, Karlsson P, Pedersen A (1989) Partial discharges in ellipsoidal and spheroidal voids. IEEE Trans Electr Insul 24(2):335–342
Fothergill JC (2007) Ageing, space charge and nanodielectrics: ten things we don’t know about dielectrics. In: 2007 IEEE international conference on solid dielectrics. IEEE, pp 1–10
Zhang Y, He L, Zhu H (2017) Influencing factors of partial discharge of needle-plate based on acoustic emission detection. In: World conference on acoustic emission. Springer, pp 389–397
Krivda A (1995) Automated recognition of partial discharges. IEEE Trans Dielectr Electr Insul 2(5):796–821
Sahoo N, Salama M, Bartnikas R (2005) Trends in partial discharge pattern classification: a survey. IEEE Trans Dielectr Electr Insul 12(2):248–264
Lin C-F, Wang S-D (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464–471
Duda RO, Hart PE, Stork DG (2001) Pattern classification, vol 58, 2nd edn. Wiley, New York, p 16
Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118
Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139
Wu M, Cao H, Cao J, Nguyen H-L, Gomes JB, Krishnaswamy SP (2015) An overview of state-of-the-art partial discharge analysis techniques for condition monitoring. IEEE Electr Insul Mag 31(6):22–35
Catterson V, Sheng B (2015) Deep neural networks for understanding and diagnosing partial discharge data. In: 2015 IEEE electrical insulation conference (EIC). IEEE, pp 218–221
Lu S, Chai H, Sahoo A, Phung B (2020) Condition monitoring based on partial discharge diagnostics using machine learning methods: a comprehensive state-of-the-art review. IEEE Trans Dielectr Electr Insul 27(6):1861–1888
Wang Y, Yan J, Yang Z, Liu T, Zhao Y, Li J (2019) Partial discharge pattern recognition of gas-insulated switchgear via a light-scale convolutional neural network. Energies 12(24):4674
Barrios S, Buldain D, Comech MP, Gilbert I, Orue I (2019) Partial discharge classification using deep learning methods-survey of recent progress. Energies 12(13):2485
Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller P-A (2019) Deep learning for time series classification: a review. Data Min Knowl Discov 33(4):917–963
Ibrahim A, Zhou Y, Jenkins ME, Trejos AL, Naish MD (2020) The design of a parkinson’s tremor predictor and estimator using a hybrid convolutional-multilayer perceptron neural network. In: 2020 42nd annual international conference of the ieee engineering in medicine & biology society (EMBC). IEEE, pp 5996–6000
Khan MA, Choo J, Kim Y-H (2019) End-to-end partial discharge detection in power cables via time-domain convolutional neural networks. J Electr Eng Technol 14(3):1299–1309
Wang W, Yu N (2020) Partial discharge detection with convolutional neural networks. In: 2020 international conference on probabilistic methods applied to power systems (PMAPS). IEEE, pp 1–6
Peng X, Yang F, Wang G, Wu Y, Li L, Li Z, Bhatti AA, Zhou C, Hepburn DM, Reid AJ et al (2019) A convolutional neural network-based deep learning methodology for recognition of partial discharge patterns from high-voltage cables. IEEE Trans Power Deliv 34(4):1460–1469
Borghei M, Ghassemi M, Kordi B, Gill P, Oliver D (2021) A finite element analysis model for internal partial discharges in an air-filled cylindrical cavity inside solid dielectric. In: IEEE electrical insulation conference (EIC), pp 7–21
Contin A, Cavallini A, Montanari G, Pasini G, Puletti F (2002) Digital detection and fuzzy classification of partial discharge signals. IEEE Trans Dielectr Electr Insul 9(3):335–348
Cavallini A, Montanari G, Contin A, Pulletti F (2003) A new approach to the diagnosis of solid insulation systems based on pd signal inference. IEEE Electr Insul Mag 19(2):23–30
Cavallini A, Montanari G, Puletti F, Contin A (2005) A new methodology for the identification of pd in electrical apparatus: properties and applications. IEEE Trans Dielectr Electr Insul 12(2):203–215
Janani H (2016) Partial discharge source classification using pattern recognition algorithms. PhD thesis, University of Manitoba
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105
Shen X, Ni Z, Liu L, Yang J, Ahmed K (2021) Wipass: 1d-cnn-based smartphone keystroke recognition using wifi signals. Pervasive Mob Comput 73:101393
Chen W, Shi K (2019) A deep learning framework for time series classification using relative position matrix and convolutional neural network. Neurocomputing 359:384–394
Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Icml
Goodfellow I, Lee H, Le Q, Saxe A, Ng A (2009) Measuring invariances in deep networks. Adv Neural Inf Process Syst 22:646–654
Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. Preprint arXiv:1412.7062
Yi-de M, Qing L, Zhi-Bai Q (2004) Automated image segmentation using improved pcnn model based on cross-entropy. In: Proceedings of 2004 international symposium on intelligent multimedia, video and speech processing. IEEE, pp 743–746
Zhang Z (2018) Improved adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS). IEEE, pp 1–2
Burman P (1989) A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika 76(3):503–514
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828
Jetley S, Lord NA, Lee N, Torr PH (2018) Learn to pay attention. Preprint arXiv:1804.02391
Du M, Liu N, Hu X (2019) Techniques for interpretable machine learning. Commun ACM 63(1):68–77
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626
He T, Guo J, Chen N, Xu X, Wang Z, Fu K, Liu L, Yi Z (2019) Medimlp: using grad-cam to extract crucial variables for lung cancer postoperative complication prediction. IEEE J Biomed Health Inform 24(6):1762–1771
Panwar H, Gupta P, Siddiqui MK, Morales-Menendez R, Bhardwaj P, Singh V (2020) A deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images. Chaos Solitons Fractals 140:110190
Cian D, van Gemert J, Lengyel A (2020) Evaluating the performance of the lime and grad-cam explanation methods on a lego multi-label image classification task. Preprint arXiv:2008.01584
Feng F, Wu C, Zhu J, Wu S, Tian Q, Jiang P (2020) Research on multitask fault diagnosis and weight visualization of rotating machinery based on convolutional neural network. J Braz Soc Mech Sci Eng 42(11):1–14
Acknowledgements
The authors are thankful to Dr. Hamed Janani for his useful comments and discussion.
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Financial support from Natural Sciences and Engineering Research Council of Canada (NSERC) and the Faculty of Graduate Studies, University of Manitoba is acknowledged.
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Conceptualization was contributed by SM, PG, DO, AA, BK; methodology was contributed by SM, PG, DO, AA, BK; software was contributed by SM; formal analysis was contributed by SM, AA, BK; investigation was contributed by SM, PG, DO, AA, BK; original draft preparation was contributed by SM; writing was contributed by SM; review and editing was contributed by SM, DO, AA, BK. All authors have read and agreed to the published version of the manuscript.
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Mantach, S., Gill, P., Oliver, D.R. et al. An interpretable CNN model for classification of partial discharge waveforms in 3D-printed dielectric samples with different void sizes. Neural Comput & Applic 34, 11739–11750 (2022). https://doi.org/10.1007/s00521-022-07066-y
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DOI: https://doi.org/10.1007/s00521-022-07066-y