{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T22:11:46Z","timestamp":1769206306931,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Grid Shanxi Electric Power Company","award":["5205M0230008"],"award-info":[{"award-number":["5205M0230008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>High-impedance fault (HIF) feeder detection in resonant-grounded active distribution systems remains a challenging issue. In practice, fault currents are typically weak, and the integration of distributed generation (DG) often distorts fault signatures, significantly limiting the effectiveness of existing detection techniques. This paper presents a novel HIF feeder detection method based on the fusion of zero-sequence current (ZSC) cross-correlation polarity analysis and harmonic wavebody similarity matching. Firstly, the HIF mechanism is examined, and the impact of DG on ZSC behavior is characterized, revealing polarity differences among feeders. To suppress high-frequency interference, variational mode decomposition (VMD) is employed to extract low-frequency components indicative of ZSC polarity, which are then subjected to cross-correlation analysis and used as the primary detection indicator. When ZSCs are heavily distorted due to DG, harmonic wavebody similarity serves as a supplementary detection feature. A comprehensive detection criterion is subsequently formulated by combining both analyses. Simulation and experimental results demonstrate that under HIF conditions, the proposed method is robust against variations in fault location, fault type, and noise interference, and can accurately identify the faulty feeder. Moreover, it remains effective for arc grounding, grass grounding, and pond grounding scenarios, highlighting its strong practical applicability.<\/jats:p>","DOI":"10.3390\/info16110967","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T14:55:15Z","timestamp":1762527315000},"page":"967","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A High-Impedance Fault Feeder Detection Method for Resonant Grounded Active Distribution Systems Based on Polarity and Harmonic Wavebody Similarity"],"prefix":"10.3390","volume":"16","author":[{"given":"Tong","family":"Lu","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sizu","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","first-page":"3530811","article-title":"Faulty Feeder Detection under High Impedance Fault for Active Distribution Network in Resonant Grounding Mode","volume":"73","author":"Wang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10932","DOI":"10.1109\/ACCESS.2024.3352258","article-title":"Active High-Impedance Fault Detection Method for Resonant Grounding Distribution Networks","volume":"12","author":"Yao","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, Y., Cao, J., Hu, Z., Han, X., and Zhou, X. (2023). Faulty Feeder Detection Based on Grey Correlation Degree of Adaptive Frequency Band in Resonant Grounding Distribution System. Sustainability, 15.","DOI":"10.3390\/su15108116"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.egyr.2021.01.058","article-title":"Research on Correlation Factor Analysis and Prediction Method of Overhead Transmission Line Defect State Based on Association Rule Mining and RBF-SVM","volume":"7","author":"Wang","year":"2021","journal-title":"Energy Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109706","DOI":"10.1016\/j.ijepes.2023.109706","article-title":"Fault Nature Identification and Location Scheme for Distribution Network Based on Active Injection from IIDG","volume":"156","author":"Luo","year":"2024","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3968","DOI":"10.1109\/TPWRD.2022.3142186","article-title":"Faulty Feeder Detection for Single-Phase-to-Ground Fault in Distribution Networks Based on Transient Energy and Cosine Similarity","volume":"37","author":"Wei","year":"2022","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhao, R., Lu, J., Yu, Z., Wu, Y., and Wang, K. (2024). A Two-Stage Fault Localization Method for Active Distribution Networks Based on COA-SVM Model and Cosine Similarity. Electronics, 13.","DOI":"10.3390\/electronics13193809"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1109\/TSG.2022.3147044","article-title":"Nonlinear Modeling Analysis and Arc High-Impedance Faults Detection in Active Distribution Networks with Neutral Grounding via Petersen Coil","volume":"13","author":"Wang","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gogula, V., and Edward, B. (2024). Advanced Signal Analysis for High-Impedance Fault Detection in Distribution Systems: A Dynamic Hilbert Transform Method. Front. Energy Res., 12.","DOI":"10.3389\/fenrg.2024.1365538"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109391","DOI":"10.1016\/j.epsr.2023.109391","article-title":"Series arc Fault Identification Method Based on Wavelet Transform and Feature Values Decomposition Fusion DNN","volume":"221","author":"Gong","year":"2023","journal-title":"Electr. Power Syst. Res."},{"key":"ref_11","first-page":"3508409","article-title":"Series arc Fault Detection Based on Wavelet Compression Reconstruction data Enhancement and Deep Residual Network","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11115","DOI":"10.1109\/JIOT.2021.3131171","article-title":"Fa-Mb-ResNet for Grounding Fault Identification and Line Selection in the Distribution Networks","volume":"9","author":"Yang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"77445","DOI":"10.1007\/s11042-024-18335-4","article-title":"An Empirical Wavelet Transform Based Fault Detection and Hybrid Convolutional Recurrent Neural Network for Fault Classification in Distribution Network Integrated Power System","volume":"83","author":"Mampilly","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nsaif, Y.M., Lipu, M.S.H., Hussain, A., Ayob, A., Yusof, Y., and Zainuri, M. (2022). A New Voltage Based Fault Detection Technique for Distribution Network Connected to Photovoltaic Sources Using Variational Mode Decomposition Integrated Ensemble Bagged Trees Approach. Energies, 15.","DOI":"10.3390\/en15207762"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/JSEN.2023.3330970","article-title":"Detection of High-Impedance Fault in Distribution Networks using Frequency-Band Energy Curve","volume":"24","author":"Bai","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, M., and Liu, H. (2023). Overlap Functions-Based Fuzzy Mathematical Morphological Operators and Their Applications in Image Edge Extraction. Fractal Fract., 7.","DOI":"10.3390\/fractalfract7060465"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1109\/TPWRD.2020.3011930","article-title":"Distortion-Based Detection of High Impedance Fault in Distribution Systems","volume":"36","author":"Wei","year":"2020","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cao, Y., Tang, J., Shi, S., Cai, D., Zhang, L., and Xiong, P. (2024). Fault Diagnosis Techniques for Electrical Distribution Network Based on Artificial Intelligence and Signal Processing: A review. Processes, 13.","DOI":"10.3390\/pr13010048"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Bandara, S., Rajeev, P., and Gad, E. (2023). Power distribution system faults and wildfires: Mechanisms and prevention. Forests, 14.","DOI":"10.3390\/f14061146"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1109\/TPWRD.2022.3203992","article-title":"Faulty feeder detection for single line-to-ground fault in distribution networks with DGs based on correlation analysis and harmonics energy","volume":"38","author":"Yuan","year":"2022","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1109\/TSG.2020.3026390","article-title":"Faulty Feeder Detection Based on Fundamental Component Shift and Multiple-Transient-Feature Fusion in Distribution Networks","volume":"12","author":"Wei","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15410","DOI":"10.1109\/ACCESS.2024.3354177","article-title":"Low-Voltage arc Fault Identification Using a Hybrid Method Based on Improved Salp Swarm Algorithm\u2013Variational Mode Decomposition\u2013Random Forest","volume":"12","author":"Li","year":"2024","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ye, J., Bao, W., Liao, C., Chen, D., and Hu, H. (2023). Corn phenology detection using the derivative dynamic time warping method and sentinel-2 time series. Remote Sens., 15.","DOI":"10.3390\/rs15143456"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9001909","DOI":"10.1109\/TIM.2024.3368477","article-title":"High Impedance Faults Detection in Distribution Systems Using Signal Correlations","volume":"73","author":"Farrokhniya","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2462","DOI":"10.1109\/TPWRD.2018.2799181","article-title":"A Decentralized Fault Detection Technique for Detecting Single Phase to Ground Faults in Power Distribution Systems with Resonant Grounding","volume":"33","author":"Barik","year":"2018","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4712","DOI":"10.1109\/TIM.2019.2954009","article-title":"A novel approach based on CEEMDAN to select the faulty feeder in neutral resonant grounded distribution systems","volume":"69","author":"Jin","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3783","DOI":"10.1109\/TSG.2016.2642988","article-title":"High-impedance fault detection based on nonlinear voltage\u2013current characteristic profile identification","volume":"9","author":"Wang","year":"2016","journal-title":"IEEE Trans. Smart Grid"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/967\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T05:15:40Z","timestamp":1762838140000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/967"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":28,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["info16110967"],"URL":"https:\/\/doi.org\/10.3390\/info16110967","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]}}}