{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:34:47Z","timestamp":1774967687997,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National key R &amp; D project","award":["2020yfb2009405"],"award-info":[{"award-number":["2020yfb2009405"]}]},{"name":"National key R &amp; D project","award":["cstc2021jcyj-msxmX0891"],"award-info":[{"award-number":["cstc2021jcyj-msxmX0891"]}]},{"name":"Chongqing Nature Science Foundation","award":["2020yfb2009405"],"award-info":[{"award-number":["2020yfb2009405"]}]},{"name":"Chongqing Nature Science Foundation","award":["cstc2021jcyj-msxmX0891"],"award-info":[{"award-number":["cstc2021jcyj-msxmX0891"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The frequent occurrence of single-phase grounding faults affects the reliable operation of power systems. When a single-phase grounding fault occurs, it is difficult to accurately identify the fault type because of the weak characterization and subtle distinction between different fault types. Therefore, this paper proposes a single-phase grounding fault type identification method based on the multi-feature transformation and fusion. Firstly, the Hilbert\u2013Huang transform (HHT) was used to preprocess the fault recorded wave data to highlight the characteristics between different fault types. Secondly, the deep learning model ResNet18 and the long short-term memory (LSTM) are designed to extract the complex abstract features and time-series correlation features from the preprocessed data set separately. Finally, it designs a fusion model to combine the advantages of heterogeneous models to identify the type of single-phase grounding fault. Experiments validate that the method is good at fully mining the characteristics of the fault types contained in the fault recorded wave data, so it can identify multiple types of faults with strong robustness and provide a reliable basis for the subsequent formulation of targeted fault-handling measures.<\/jats:p>","DOI":"10.3390\/s22093521","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T02:46:39Z","timestamp":1651805199000},"page":"3521","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion"],"prefix":"10.3390","volume":"22","author":[{"given":"Min","family":"Fan","sequence":"first","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, China"}]},{"given":"Jialu","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, China"}]},{"given":"Xinyu","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, China"}]},{"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"ref_1","unstructured":"Zhang, Z. (2014). Theoretical Research on Single-Phase Grounding Fault Line Selection in Small Current Grounding System, Liaoning Science and Technology Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107283","DOI":"10.1016\/j.ijepes.2021.107283","article-title":"Local current-based method for fault identification and location on series capacitor-compensated transmission line with different configurations","volume":"133","author":"Elmitwally","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105910","DOI":"10.1016\/j.ijepes.2020.105910","article-title":"High-impedance ground faulted line-section location method for a resonant grounding system based on the zero-sequence current\u2019s declining periodic component","volume":"19","author":"Li","year":"2020","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, K., Zhang, S., Li, B., Zhang, C., Liu, B., Jin, H., and Zhao, J. (2021). Faulty Feeder Identification Based on Data Analysis and Similarity Comparison for Flexible Grounding System in Electric Distribution Networks. Sensors, 21.","DOI":"10.3390\/s21010154"},{"key":"ref_5","first-page":"685","article-title":"Online criterion and identification of single-phase ground fault with high resistence in distribution network","volume":"36","author":"Zhou","year":"2015","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_6","first-page":"1098","article-title":"Identification of single-phase arc grounding fault in power distribution network","volume":"51","author":"Shen","year":"2018","journal-title":"J. Wuhan Univ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3783","DOI":"10.1109\/TSG.2016.2642988","article-title":"High-Impedance Fault Detection Based on Nonlinear Voltage-Current Characteristic Profile Identification","volume":"9","author":"Wang","year":"2016","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_8","first-page":"83","article-title":"High impedance fault identification method of distribution network","volume":"36","author":"Chen","year":"2013","journal-title":"J. Chongqing Univ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ji, P., Pei, Y., Zhao, S., Bai, C., Wu, B., Liang, L., Pin, D., Sun, H., and Zhi, T. (2018, January 9\u201311). A Novel Location Method for Single-phase Grounding Fault for Distribution Network Based on Transient Technique. Proceedings of the 2018 Chinese Control And Decision Conference (CCDC), Shenyang, China.","DOI":"10.1109\/CCDC.2018.8408033"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1109\/TIA.2012.2190819","article-title":"High-Performance Arcing-Fault Location in Distribution Networks","volume":"48","author":"Zhou","year":"2012","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_11","first-page":"8202","article-title":"Identification of Single-phase High Resistance Earth Fault in Distribution Network Based on Wavelet Packet Energy Ratio of Zero Sequence Voltage","volume":"20","author":"Chen","year":"2020","journal-title":"Sci. Technol. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shao, W., Bai, J., Cheng, Y., Zhang, Z., and Li, N. (2019). Research on a Faulty Line Selection Method Based on the Zero-Sequence Disturbance Power of Resonant Grounded Distribution Networks. Energies, 12.","DOI":"10.3390\/en12050846"},{"key":"ref_13","first-page":"59","article-title":"Grounding Faulty line detection based on transient waveform difference recognition for resonant earthed system","volume":"34","author":"Guo","year":"2014","journal-title":"Electr. Power Autom. Equip."},{"key":"ref_14","first-page":"7","article-title":"Detection method of high impedance grounding fault based on differential current of zero-sequence current projection and neutral point current in low-resistance grounding system","volume":"39","author":"Sheng","year":"2019","journal-title":"Electr. Power Autom. Equip."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106198","DOI":"10.1016\/j.ijepes.2020.106198","article-title":"Single-phase-to-ground fault section location in flexible resonant grounding distribution networks using soft open points","volume":"122","author":"Li","year":"2020","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nakho, A., and Hamam, Y. (2021, January 27\u201329). Detection and Classification of Single Phase to Ground Faults Under High Resistance Ground Paths in Power Systems using Machine Learning. Proceedings of the 2021 Southern African Universities Power Engineering Conference\/Robotics and Mechatronics\/Pattern Recognition Association of South Africa (SAUPEC\/RobMech\/PRASA), Potchefstroom, South Africa.","DOI":"10.1109\/SAUPEC\/RobMech\/PRASA52254.2021.9377237"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106991","DOI":"10.1016\/j.compchemeng.2020.106991","article-title":"Fault detection and identification using Bayesian recurrent neural networks","volume":"141","author":"Sun","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"012008","DOI":"10.1088\/1742-6596\/1656\/1\/012008","article-title":"Single-phase ground fault identification method for distribution network based on inception model and sample expansion","volume":"1656","author":"Zhu","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_19","unstructured":"Tong, Y., You, Z., and Shu, L. (2012). Analysis of Intermittent Arc Overvoltages in Low Resistance Grounded Systems, The Chinese Society of Universities for Electric Power System and Its Automation."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, Y., Zhang, Z., Zeng, Z., and Cao, Y. (2019, January 21\u201323). XGBoost Classifier for Fault Identification in Low Voltage Neutral Point Ungrounded System. Proceedings of the 2019 IEEE Sustainable Power and Energy Conference (iSPEC), Beijing, China.","DOI":"10.1109\/iSPEC48194.2019.8974996"},{"key":"ref_21","first-page":"1829","article-title":"Identifying Single-Phase-to-Ground Fault Feeder in Neutral Noneffectively Grounded Distribution System Using Wavelet Transform","volume":"23","author":"Dong","year":"2011","journal-title":"Electr. Power Sci. Eng."},{"key":"ref_22","first-page":"118","article-title":"Criterion Based on the Fault Component of Zero Sequence Current for Online Fault Location of Single-phase Fault in Distribution Network","volume":"30","author":"Ni","year":"2010","journal-title":"Proc. CSEE"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liang, J. (2019, January 22\u201324). Research on Rapid Diagnosis Method of Single-Phase Grounding Fault in Distribution Network Based on Deep Learning. Proceedings of the 2019 Chinese Automation Congress (CAC), Hangzhou, China.","DOI":"10.1109\/CAC48633.2019.8997225"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107100","DOI":"10.1016\/j.compeleceng.2021.107100","article-title":"A novel three-dimensional deep learning algorithm for classification of power system faults","volume":"91","author":"Srikanth","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, Y., Yan, H., and Shi, F. (2019, January 10\u201312). Single-phase-to-ground Fault Diagnosis Based on Waveform Feature Extraction and Matrix Analysis. Proceedings of the 2019 9th International Conference on Power and Energy Systems (ICPES), Perth, Australia.","DOI":"10.1109\/ICPES47639.2019.9105483"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhong, B., and Li, Y. (2019, January 5\u20137). Image Feature Point Matching Based on Improved SIFT Algorithm. Proceedings of the 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), Xiamen, China.","DOI":"10.1109\/ICIVC47709.2019.8981329"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, P., Shen, L., Huang, X., and Xin, Q. (2020, January 25\u201327). Application of an Improved SIFT algorithm in GPR images. Proceedings of the 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Harbin, China.","DOI":"10.1109\/ICMCCE51767.2020.00477"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107188","DOI":"10.1016\/j.compeleceng.2021.107188","article-title":"Feature extraction based on Gabor filter and Support Vector Machine classifier in defect analysis of Thermoelectric Cooler Component","volume":"92","author":"Zhao","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_29","first-page":"1","article-title":"HOG-ShipCLSNet: A Novel Deep Learning Network With HOG Feature Fusion for SAR Ship Classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","unstructured":"Rizal, R.A., Sihotang, J.S., and Gultom, R. (2019, January 28\u201329). Comparison of SURF and HOG extraction in classifying the blood image of malaria parasites using SVM. Proceedings of the 2019 International Conference of Computer Science and Information Technology (ICoSNIKOM), Medan, Indonesia."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, Z., Peng, D., Zuo, M.J., Xia, J., and Qin, Y. (2021). Improved Hilbert\u2013Huang transform with soft sifting stopping criterion and its application to fault diagnosis of wheelset bearings. ISA Trans.","DOI":"10.1016\/j.isatra.2021.07.011"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100606","DOI":"10.1016\/j.segan.2022.100606","article-title":"Hilbert-Huang transform and decision tree based islanding and fault recognition in renewable energy penetrated distribution system","volume":"30","author":"Shaik","year":"2022","journal-title":"Sustain. Energy Grids Netw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.precisioneng.2021.01.009","article-title":"A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform","volume":"3","author":"Mahata","year":"2021","journal-title":"Precis. Eng."},{"key":"ref_34","first-page":"18","article-title":"Distribution Feeder Ground Fault Location Based on Hilbert-Huang Transform","volume":"15","author":"Wang","year":"2020","journal-title":"J. Electr. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tan, Y., Li, Y., Liu, H., Lu, W., and Xiao, X. (2020, January 27\u201329). Performance Comparison of Data Classification based on Modern Convolutional Neural Network Architectures. Proceedings of the 2020 39th Chinese Control Conference (CCC), Shenyang, China.","DOI":"10.23919\/CCC50068.2020.9189237"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103834","DOI":"10.1016\/j.autcon.2021.103834","article-title":"A deep neural network-based method for deep information extraction using transfer learning strategies to support automated compliance checking","volume":"132","author":"Zhang","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"219","DOI":"10.3233\/FI-2019-1829","article-title":"Abnormality Diagnosis in Mammograms by Transfer Learning Based on ResNet18","volume":"168","author":"Yu","year":"2019","journal-title":"Fundam. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2016, January 19\u201322). Wide Residual Networks. Proceedings of the 2016 British Machine Vision Conference, York, UK.","DOI":"10.5244\/C.30.87"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103498","DOI":"10.1016\/j.compind.2021.103498","article-title":"A survey on anomaly detection for technical systems using LSTM networks","volume":"131","author":"Lindemann","year":"2021","journal-title":"Comput. Ind."},{"key":"ref_40","first-page":"100","article-title":"Identification Method for Single-phase Ground Fault of Distribution Network Based on LSTM Model","volume":"32","author":"Shao","year":"2019","journal-title":"Guangdong Electr. Power"},{"key":"ref_41","unstructured":"Yue, Y., Li, X., and Zong, Q. (2011, January 9\u201311). Development of automobile fault diagnosis expert system based on fault tree\u2014Neural network ensamble. Proceedings of the 2011 International Conference on Electronics, Communications and Control (ICECC), Ningbo, China."},{"key":"ref_42","first-page":"9","article-title":"Research on single-phase ground fault based on DSP and wavelet transform","volume":"5","author":"Wu","year":"2007","journal-title":"Autom. Instrum."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sun, B., Zhang, H., and Shi, F. (2019, January 15\u201317). Machine Learning Based Fault Type Identification In the Active Distribution Network. Proceedings of the 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China.","DOI":"10.1109\/ITNEC.2019.8729054"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107178","DOI":"10.1016\/j.ijepes.2021.107178","article-title":"Fault zone identification and phase selection for microgrids using decision trees ensemble","volume":"132","author":"Saleh","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_45","unstructured":"Francis, F.B., Ronald, W., Peter, N., and Josephine, N. (2021, January 23\u201325). A Modified Decision Tree and its Application to Assess Variable Importance. Proceedings of 2021 4th International Conference on Data Science and Information Technology (DSIT 2021), Shanghai, China."},{"key":"ref_46","unstructured":"Zhang, L., Liang, Y., Sun, Y., Xue, Y., Li, L., and Jin, X. (2018, January 17\u201319). Fault Type Recognition of Over-head Lines of Distribution Networks Based on Fault Indicator Waveform Data. Proceedings of the 2018 China International Conference on Electricity Distribution (CICED), Tianjin, China."},{"key":"ref_47","first-page":"51","article-title":"Single-phase ground fault electrical model based on wavelet transform and its PSCAD\/EMTDC simulation study","volume":"39","author":"Fan","year":"2011","journal-title":"Power Syst. Prot. Control"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"109178","DOI":"10.1016\/j.measurement.2021.109178","article-title":"Multiscale feature extraction from the perspective of graph for hob fault diagnosis using spectral graph wavelet transform combined with improved random forest","volume":"176","author":"Dong","year":"2021","journal-title":"Measurement"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3521\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:06:34Z","timestamp":1760137594000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3521"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,5]]},"references-count":48,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093521"],"URL":"https:\/\/doi.org\/10.3390\/s22093521","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,5]]}}}