{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T12:02:34Z","timestamp":1777896154905,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technology Project of State Grid Tianjin Electric Power Company","award":["High Voltage-R&D 2024-01"],"award-info":[{"award-number":["High Voltage-R&D 2024-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In order to address the issue of the accuracy of partial discharge pattern recognition being constrained by unbalanced samples and the deep structure of the deep learning network, a method for partial discharge data enhancement and pattern recognition based on a convolutional autoencoder auxiliary classifier generative adversarial network (CAE-ACGAN) and a residual network (ResNet) is proposed. The initial step involves the preprocessing of the acquired partial discharge signals, with the phase resolved partial discharge (PRPD) spectra subsequently employed as the training samples. Secondly, a CAE-ACGAN is constructed. The model combines the advantages of a deep convolutional self-coding structure and a generative adversarial paradigm to generate high-quality phase resolved partial discharge spectrograms. Subsequently, a ResNet is employed as the classifier for partial discharge pattern recognition, utilising the CAE-ACGAN-enhanced partial discharge dataset for network training to achieve accurate recognition of partial discharge signals. The experimental findings demonstrate that the SSIM and PSNR indexes of the CAE-ACGAN model utilised in this study are 0.92 and 45.88 dB, respectively. The partial discharge pattern method employing the CAE-ACGAN and ResNet exhibits superiority in identifying partial discharges, attaining an identification accuracy of 98%, which is 7.25% higher than the pre-enhancement level.<\/jats:p>","DOI":"10.3390\/sym17010055","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T14:21:12Z","timestamp":1735654872000},"page":"55","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Partial Discharge Data Enhancement and Pattern Recognition Method Based on a CAE-ACGAN and ResNet"],"prefix":"10.3390","volume":"17","author":[{"given":"Songyuan","family":"Li","sequence":"first","affiliation":[{"name":"State Grid Tianjin Electric Power Company Electric Power Research Institute, Tianjin 300384, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaopeng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Tianjin High Voltage Company, Tianjin 300143, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Han","sequence":"additional","affiliation":[{"name":"State Grid Tianjin Electric Power Company Electric Power Research Institute, Tianjin 300384, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junji","family":"Feng","sequence":"additional","affiliation":[{"name":"State Grid Tianjin High Voltage Company, Tianjin 300143, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Yin","sequence":"additional","affiliation":[{"name":"State Grid Tianjin Electric Power Company Electric Power Research Institute, Tianjin 300384, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electric Power Engineering, North China Electric Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weipeng","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Electric Power Engineering, North China Electric Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6657-0880","authenticated-orcid":false,"given":"Jun","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Electric Power Engineering, North China Electric Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.egyr.2022.05.150","article-title":"Research and application of intermittent partial discharge characteristics and easy-warning system for electric equipment","volume":"8","author":"Ma","year":"2022","journal-title":"Energy Rep."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Melo, J.V.J., Lira, G.R.S., Costa, E.G., Vilar, P.B., Andrade, F.L.M., Marotti, A.C.F., Costa, A.I., Leite Neto, A.F., and Santos J\u00fanior, A.C.d. (2024). Separation and Classification of Partial Discharge Sources in Substations. Energies, 17.","DOI":"10.3390\/en17153804"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"114947","DOI":"10.1016\/j.measurement.2024.114947","article-title":"Comparative analysis of machine learning and deep learning techniques on classification of artificially created partial discharge signal","volume":"235","author":"Sahoo","year":"2024","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sekatane, P.M., and Bokoro, P. (2023). Partial Discharge Localization through k-NN and SVM. Energies, 16.","DOI":"10.3390\/en16217430"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"633","DOI":"10.4028\/www.scientific.net\/AMR.468-471.633","article-title":"A SVM model for identifying partial discharge based on TEV data","volume":"468","author":"Fei","year":"2012","journal-title":"Adv. Mater. Res."},{"key":"ref_6","first-page":"012051","article-title":"SVM-based partial discharge pattern classification for, G.I.S","volume":"Volume 960","author":"Ling","year":"2018","journal-title":"Journal of Physics: Conference Series"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"04016","DOI":"10.1051\/matecconf\/20166304016","article-title":"Research of Partial Discharge Monitoring for Switchgear Cabinet Based on LSSVM","volume":"Volume 63","author":"Han","year":"2012","journal-title":"MATEC Web of Conferences"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","article-title":"Inceptiontime: Finding alexnet for time series classification","volume":"34","author":"Fawaz","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.compeleceng.2019.03.004","article-title":"Modified Alexnet architecture for classification of diabetic retinopathy images","volume":"76","author":"Shanthi","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.buildenv.2018.05.026","article-title":"Image retrieval using BIM and features from pretrained VGG network for indoor localization","volume":"140","author":"Ha","year":"2018","journal-title":"Build. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhu, J., Zhang, Y., He, B., Li, Z., Xiong, L., and Lang, X. (2024). Improved second-harmonic imaging of ultrasound contrast agents using VGG-style network with adaptively decomposed ultrasound RF signals. Biomed. Signal Process. Control, 97.","DOI":"10.1016\/j.bspc.2024.106712"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neucom.2021.01.122","article-title":"Stable and compact design of Memristive GoogLeNet neural network\u2013ScienceDirect","volume":"441","author":"Hr","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s00521-021-06372-1","article-title":"Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4","volume":"34","author":"Habaebi","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.jmsy.2021.10.006","article-title":"Industry 4.0 and Industry 5.0\u2014Inception, conception and perception","volume":"61","author":"Xu","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_15","first-page":"108887","article-title":"Deep learning for power quality","volume":"214","author":"Bollen","year":"2021","journal-title":"Electr. Power Syst. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"345","DOI":"10.35833\/MPCE.2021.000058","article-title":"A review of graph neural networks and their applications in power systems","volume":"10","author":"Liao","year":"2022","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4295384","DOI":"10.1155\/2023\/4295384","article-title":"Multilayer deep deterministic policy gradient for static safety and stability analysis of novel power systems","volume":"2023","author":"Long","year":"2023","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5151","DOI":"10.1109\/ACCESS.2024.3350555","article-title":"Partial discharge detection based on ultrasound using optimized deep learning approach","volume":"12","author":"Alshalawi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1109\/TDEI.2020.009070","article-title":"Condition monitoring based on partial discharge diagnostics using machine learning methods: A comprehensive state-of-the-art review","volume":"27","author":"Lu","year":"2020","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","article-title":"Wider or deeper: Revisiting the resnet model for visual recognition","volume":"90","author":"Wu","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shafiq, M., and Gu, Z. (2022). Deep residual learning for image recognition: A survey. Appl. Sci., 12.","DOI":"10.3390\/app12188972"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jung, H., Kim, Y.T., Lee, S.K., and Ahn, J.H. (2024). Study on Deep-Learning Model for Phase Resolved Partial Discharge Pattern Classification Based on Convolutional Neural Network Algorithm. J. Electr. Eng. Technol., 1\u20136.","DOI":"10.1007\/s42835-024-01967-9"},{"key":"ref_23","first-page":"5044","article-title":"Data Augmentation and Pattern Recognition for Multi-sources Partial Discharge Based on Boundary Equilibrium Generative Adversarial Network with Auxiliary Classifier","volume":"41","author":"Zhu","year":"2021","journal-title":"Proc. CSEE"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ni, L., Chen, J., Chen, G., Zhao, D., Wang, G., and Aphale, S.S. (2024). An explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresis. Engineering Applications of Artificial Intelligence, Elsevier. Part A.","DOI":"10.1016\/j.engappai.2024.108904"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"24524","DOI":"10.1109\/TITS.2022.3210170","article-title":"Du-Bus: A Realtime Bus Waiting Time Estimation System Based on Multi-Source Data","volume":"23","author":"Rong","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1049\/hve2.12274","article-title":"A novel method for small and unbalanced sample pattern recognition of gas insulated switchgear partial discharge using an auxiliary classifier generative adversarial network","volume":"8","author":"Jing","year":"2023","journal-title":"High Volt."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2533","DOI":"10.1109\/TII.2017.2696534","article-title":"Imbalance learning machine-based power system short-term voltage stability assessment","volume":"13","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","article-title":"A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance","volume":"505","author":"Elreedy","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1109\/TEVC.2023.3257230","article-title":"A survey on unbalanced classification: How can evolutionary computation help?","volume":"28","author":"Pei","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, D., Li, Y., Hou, M., Liu, J., Zhao, Z., Guo, A., Zhao, H., and Deng, W. Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data. Struct. Health Monit., 2024.","DOI":"10.1177\/14759217241254121"},{"key":"ref_32","first-page":"1505","article-title":"Data augmentation method for power transformer fault diagnosis based on conditional Wasserstein generative adversarial network","volume":"44","author":"Liu","year":"2020","journal-title":"Power Syst. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"14431","DOI":"10.1007\/s00521-023-08482-4","article-title":"Comparison of deep convolution and least squares GANs for diabetic retinopathy image synthesis","volume":"35","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, Z., Zhang, S., Wei, G., Xiong, Z., and Yang, M. (2021). Image Semantic Segmentation Based on the GAN Auxiliary Network. 2021 IEEE International Conference on Progress in Informatics and Computing (PIC), IEEE.","DOI":"10.1109\/PIC53636.2021.9687008"},{"key":"ref_35","unstructured":"Deshpande, I., Zhang, Z., and Schwing, A.G. (2022, January 18\u201324). Generative modeling using the sliced wasserstein distance. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA."},{"key":"ref_36","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2017). Improved training of wasserstein gans. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1166\/jmihi.2012.1083","article-title":"Performance of quality metrics for compressed medical images through mean opinion score prediction","volume":"2","author":"Kumar","year":"2012","journal-title":"J. Med. Imaging Health Inform."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/1\/55\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:58:02Z","timestamp":1760115482000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/1\/55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,31]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["sym17010055"],"URL":"https:\/\/doi.org\/10.3390\/sym17010055","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,31]]}}}