{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T14:01:10Z","timestamp":1774274470104,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurately extracting deviation features in frequency response curves, which reflect winding deformation states, and selecting appropriate machine learning algorithms are critical for achieving a precise quantitative diagnosis of winding deformation based on frequency response analysis (FRA). To address the existing challenges in transformer winding fault diagnosis, including the absence of a systematic feature evaluation framework for frequency response data and the limited recognition accuracy of machine learning models, a novel hybrid feature selection and diagnostic framework was developed. First, a high-dimensional feature pool comprising 25 numerical indices was extracted from experimental FRA curves. To eliminate feature redundancy and arbitrary selection, a hybrid mechanism integrating maximum-relevance, minimum-redundancy (mRMR) with random forest (RF) was developed to dynamically construct task-specific optimal feature subsets. Furthermore, an inverse-distance-weighted K-nearest neighbors (IKNN) model was introduced to enhance diagnostic sensitivity by accounting for feature-space distance variations. Experimental results obtained from a laboratory winding model demonstrate that the proposed mRMR-RF-IKNN model significantly outperforms traditional and optimized benchmarks across multiple macro-evaluation metrics. This study provides a systematic, intelligent screening mechanism that ensures high-precision identification of both the types and severity of faults in power transformers.<\/jats:p>","DOI":"10.3390\/a19030241","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T13:07:06Z","timestamp":1774271226000},"page":"241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A New Method for Diagnosing Transformer Winding Faults Based on mRMR-RF Feature Selection and an Inverse Distance Weighted KNN Model"],"prefix":"10.3390","volume":"19","author":[{"given":"Chenyang","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zirui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2551-1360","authenticated-orcid":false,"given":"Song","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danyu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Xie","sequence":"additional","affiliation":[{"name":"Yuanjing Energy Co., Ltd. Shanghai Branch, Shanghai 200001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Yuanjing Energy Co., Ltd. Shanghai Branch, Shanghai 200001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1049\/iet-epa.2019.0564","article-title":"State diagnosis method of transformer winding deformation based on fusing vibration and reactance parameters","volume":"14","author":"Chen","year":"2020","journal-title":"IET Electr. Power Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e70140","DOI":"10.1049\/elp2.70140","article-title":"A New Data Augmentation Model for Fault Diagnosis of Transformer Windings Under Scarce Fault Data","volume":"20","author":"Wang","year":"2026","journal-title":"IET Electr. Power Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"110173","DOI":"10.1016\/j.epsr.2024.110173","article-title":"Diagnosis method of transformer winding mechanical deformation fault based on sliding correlation of FRA and series transfer learning","volume":"229","author":"Chen","year":"2024","journal-title":"Electr. Power Syst. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, B., Peng, H., Li, Y., Wang, S., Xiong, J., and Liu, J. (2025, January 29\u201331). A New Diagnosis Model for Transformer Winding Faults Based on One-Dimensional Residual Network. Proceedings of the 2025 International Conference on Applied Electrical Engineering and Technology (AEET), Taiyuan, China.","DOI":"10.1109\/AEET66561.2025.11307041"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111433","DOI":"10.1016\/j.epsr.2025.111433","article-title":"Enhanced detection of power transformer winding faults through 3D FRA signatures and image processing techniques","volume":"242","author":"Zhao","year":"2025","journal-title":"Electr. Power Syst. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108115","DOI":"10.1016\/j.engfailanal.2024.108115","article-title":"Application of generative AI-based data augmentation technique in transformer winding deformation fault diagnosis","volume":"159","author":"Chen","year":"2024","journal-title":"Eng. Fail. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MEI.2013.6507414","article-title":"Understanding power transformer frequency response analysis signatures","volume":"29","author":"Hashemnia","year":"2013","journal-title":"IEEE Electr. Insul. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/TDEI.2016.005551","article-title":"Analysis of physical transformer circuits for frequency response interpretation and mechanical failure diagnosis","volume":"23","author":"Pham","year":"2016","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2582","DOI":"10.1109\/TPWRD.2010.2050342","article-title":"Interpretation of transformer FRA responses\u2014Part II: Influence of transformer structure","volume":"25","author":"Sofian","year":"2010","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TPWRD.2009.2014485","article-title":"Interpretation of transformer FRA responses-part I: Influence of winding structure","volume":"24","author":"Wang","year":"2009","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/MEI.2004.1342427","article-title":"Transformer winding movement monitoring in service\u2014Key factors affecting FRA measurements","volume":"20","author":"Wang","year":"2004","journal-title":"IEEE Electr. Insul. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.ijepes.2017.01.014","article-title":"FRA interpretation using numerical indices: State-of-the-art","volume":"89","author":"Samimi","year":"2017","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_13","unstructured":"(2004). Frequency Response Analysis on Winding Deformation of Power Transformers (Standard No. DL\/T911-2004)."},{"key":"ref_14","unstructured":"(2012). Power Transformers\u2014Part 18: Measurement of Frequency Response (Standard No. IEC 60076-18:2012)."},{"key":"ref_15","unstructured":"(2025). IEEE Guide for the Application and Interpretation of Frequency Response Analysis for Oil-Immersed Transformers (Standard No. C57.149-2024)."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TPWRD.2016.2572160","article-title":"Effect of Different Connection Schemes, Terminating Resistors and Measurement Impedances on the Sensitivity of the FRA Method","volume":"32","author":"Samimi","year":"2017","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.4316\/aece.2011.02004","article-title":"A New Method for Detection and Evaluation of Winding Mechanical Faults in Transformer through Transfer Function Measurements","volume":"11","author":"Bigdeli","year":"2011","journal-title":"Adv. Electr. Comput. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2374","DOI":"10.1109\/TDEI.2014.004364","article-title":"ANN and cross-correlation based features for discrimination between electrical and mechanical defects and their localization in transformer winding","volume":"21","author":"Ghanizadeh","year":"2014","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.32604\/ee.2023.030107","article-title":"Diagnosis of Disc Space Variation Fault Degree of Transformer Winding Based on K-Nearest Neighbor Algorithm","volume":"120","author":"Wang","year":"2023","journal-title":"Energy Eng."},{"key":"ref_20","first-page":"11","article-title":"Mechanical Fault Types Detection in Transformer Windings Using Interpretation of Frequency Responses via Multilayer Perceptron","volume":"11","author":"Behkam","year":"2022","journal-title":"J. Oper. Autom. Power Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112494","DOI":"10.1109\/ACCESS.2019.2932497","article-title":"Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e2666","DOI":"10.1002\/etep.2666","article-title":"Detecting the location and severity of transformer winding deformation by a novel adaptive particle swarm optimization algorithm","volume":"29","author":"Jahan","year":"2019","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shen, J., Gu, Q., Liu, J., and Peng, L. (2025). Transformer Winding Fault Diagnosis Based on Fusion of FRA and IPOA-CNN. Proceedings of the 2025 IEEE 5th New Energy and Energy Storage System Control Summit Forum (NEESSC), Hohhot, China, 15\u201317 August 2025, Institute of Electrical and Electronics Engineers Inc.","DOI":"10.1109\/NEESSC66038.2025.11199450"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/TPWRD.2020.2987205","article-title":"Analysis of Statistical Methods for Assessment of Power Transformer Frequency Response Measurements","volume":"36","author":"Tahir","year":"2021","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/978-3-030-31676-1_76","article-title":"Evaluation of Numerical Indices for Objective Interpretation of Frequency Response to Detect Mechanical Faults in Power Transformers","volume":"Volume 598","author":"Tahir","year":"2020","journal-title":"Proceedings of the 21st International Symposium on High Voltage Engineering"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106324","DOI":"10.1016\/j.epsr.2020.106324","article-title":"The actual measurement and analysis of transformer winding deformation fault degrees by FRA using mathematical indicators","volume":"184","author":"Ni","year":"2020","journal-title":"Electr. Power Syst. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1049\/iet-gtd.2016.0879","article-title":"Evaluation of numerical indices for the assessment of transformer frequency response","volume":"11","author":"Samimi","year":"2017","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Banaszak, S., Kornatowski, E., and Szoka, W. (2021). The Influence of the Window Width on FRA Assessment with Numerical Indices. Energies, 14, (In English).","DOI":"10.3390\/en14020362"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"12335","DOI":"10.1007\/s10115-025-02594-0","article-title":"On the utility of ordered incremental attribute learning-based variance and mRMR techniques","volume":"67","author":"Gorrab","year":"2025","journal-title":"Knowl. Inf. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2754","DOI":"10.1049\/ipr2.13133","article-title":"Change detection in SAR image based on weighted difference image generation and optimized random forest","volume":"18","author":"Yuan","year":"2024","journal-title":"IET Image Process."},{"key":"ref_31","unstructured":"Han, H., Guo, X., and Yu, H. (2016). Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest. Proceedings of the 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 26\u201328 August 2016, IEEE Computer Society."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tahir, M., and Tenbohlen, S. (2023). Transformer Winding Fault Classification and Condition Assessment Based on Random Forest Using FRA. Energies, 16.","DOI":"10.3390\/en16093714"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/3\/241\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T13:10:42Z","timestamp":1774271442000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/3\/241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,23]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["a19030241"],"URL":"https:\/\/doi.org\/10.3390\/a19030241","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,23]]}}}