{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:49:14Z","timestamp":1776811754610,"version":"3.51.2"},"reference-count":30,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,10,6]]},"abstract":"<jats:p>The power metering system is an important part of the smart grid for data acquisition and analysis. The fault state of the main station directly affects the stable and safe operation of the power metering system. Hinged on the real-world data supplied by the monitoring platform of the Metrology Center of Guangdong Power Grid Co., Ltd., we present a novel malfunction diagnosis method for the main station of the power metering system. The proposed method utilizes the synthetic mi-nority over-sampling technique (SMOTE) and designs a combined model of long short-term memory (LSTM) network and ResNet. SMOTE solves the sample imbalance problem. Furthermore, the combined LSTM-ResNet model employs LSTM to extract the time-dependent signal feature and exploits ResNet to optimize data flow. Consequently, the proposed LSTM-ResNet model improves training efficiency and malfunction diagnosis accuracy. The proposed diagnosis mthod is verifird on the real-world data, which proves the proposed method\u2019s surpass traditional methods. A specific analysis of results and the practical application of the proposed method is also elaborated.<\/jats:p>","DOI":"10.3233\/jcm-226883","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T10:27:09Z","timestamp":1686652029000},"page":"2621-2633","source":"Crossref","is-referenced-by-count":2,"title":["Malfunction diagnosis of main station of power metering system using LSTM-ResNet with SMOTE method"],"prefix":"10.66113","volume":"23","author":[{"given":"Qianqian","family":"Cai","sequence":"first","affiliation":[{"name":"Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, China"}]},{"given":"Yong","family":"Sun","sequence":"additional","affiliation":[{"name":"Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, China"}]},{"given":"Youpeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, China"}]},{"given":"Jingming","family":"Zhao","sequence":"additional","affiliation":[{"name":"Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, China"}]},{"given":"Jingru","family":"Li","sequence":"additional","affiliation":[{"name":"Metrology Center of Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, China"}]},{"given":"Shiqi","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Wuhan University, Wuhan, Hubei, China"}]}],"member":"55691","reference":[{"issue":"1","key":"10.3233\/JCM-226883_ref1","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/TSG.2020.3010230","article-title":"Intrusion detection for cybersecurity of smart meters","volume":"12","author":"Sun","year":"2020","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"4","key":"10.3233\/JCM-226883_ref2","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1109\/TPWRS.2016.2631891","article-title":"The 2015 ukraine blackout: Implications for false data injection attacks","volume":"32","author":"Liang","year":"2016","journal-title":"IEEE Transactions on Power Systems"},{"key":"10.3233\/JCM-226883_ref3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ijepes.2017.12.020","article-title":"Cyber security of a power grid: State-of-the-art","volume":"99","author":"Sun","year":"2018","journal-title":"International Journal of Electrical Power & Energy Systems"},{"issue":"1","key":"10.3233\/JCM-226883_ref4","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.asej.2020.05.004","article-title":"Recent advancement in smart grid technology: Future prospects in the electrical power network","volume":"12","author":"Butt","year":"2021","journal-title":"Ain Shams Engineering Journal"},{"key":"10.3233\/JCM-226883_ref5","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1016\/j.renene.2019.08.092","article-title":"A survey on smart grid technologies and applications","volume":"146","author":"Dileep","year":"2020","journal-title":"Renewable Energy"},{"issue":"11","key":"10.3233\/JCM-226883_ref6","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1016\/j.conengprac.2004.11.014","article-title":"The development of an adaptive threshold for model-based fault detection of a nonlinear electro-hydraulic system","volume":"13","author":"Shi","year":"2005","journal-title":"Control Engineering Practice"},{"issue":"4","key":"10.3233\/JCM-226883_ref7","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/TII.2013.2243743","article-title":"From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis","volume":"9","author":"Dai","year":"2013","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.3233\/JCM-226883_ref8","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.conengprac.2016.02.010","article-title":"An improved weighted recursive PCA algorithm for adaptive fault detection","volume":"50","author":"Portnoy","year":"2016","journal-title":"Control Engineering Practice"},{"issue":"4","key":"10.3233\/JCM-226883_ref9","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1109\/TII.2014.2341934","article-title":"An LWPR-based data-driven fault detection approach for nonlinear process monitoring","volume":"10","author":"Wang","year":"2014","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"6","key":"10.3233\/JCM-226883_ref10","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/S0967-0661(02)00096-5","article-title":"Recursive partial least squares algorithms for monitoring complex industrial processes","volume":"11","author":"Wang","year":"2003","journal-title":"Control Engineering Practice"},{"issue":"6","key":"10.3233\/JCM-226883_ref11","doi-asserted-by":"crossref","first-page":"6895","DOI":"10.1016\/j.eswa.2010.12.034","article-title":"A novel fault diagnosis system using pattern classification on kernel FDA subspace","volume":"38","author":"Zhu","year":"2011","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"10.3233\/JCM-226883_ref12","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1016\/j.engappai.2010.01.027","article-title":"A framework for on-line trend extraction and fault diagnosis","volume":"23","author":"Maurya","year":"2010","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1","key":"10.3233\/JCM-226883_ref13","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ces.2010.10.008","article-title":"Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS","volume":"66","author":"Zhang","year":"2011","journal-title":"Chemical Engineering Science"},{"key":"10.3233\/JCM-226883_ref14","first-page":"559","article-title":"Data mining: Concepts and techniques","volume":"10","author":"Mining","year":"2006","journal-title":"Morgan Kaufinann"},{"key":"10.3233\/JCM-226883_ref15","doi-asserted-by":"crossref","unstructured":"Tao Y, Zheng J, Wang T, Hu Y, editors. A state and fault prediction method based on RBF neural networks. In: 2016 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO). IEEE; 2016.","DOI":"10.1109\/ARSO.2016.7736285"},{"key":"10.3233\/JCM-226883_ref16","doi-asserted-by":"crossref","unstructured":"Rakhshani E, Sariri I, Rouzbehi K, editors. Application of data mining on fault detection and prediction in boiler of power plant using artificial neural network. In: 2009 International Conference on Power Engineering, Energy and Electrical Drives. IEEE; 2009.","DOI":"10.1109\/POWERENG.2009.4915186"},{"key":"10.3233\/JCM-226883_ref17","doi-asserted-by":"crossref","unstructured":"Mahdi M, Genc VI, editors. Artificial neural network based algorithm for early prediction of transient stability using wide area measurements. In: 2017 5th International Istanbul Smart Grid and Cities Congress and Fair (ICSG). IEEE; 2017.","DOI":"10.1109\/SGCF.2017.7947611"},{"issue":"3","key":"10.3233\/JCM-226883_ref18","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/TNNLS.2016.2551940","article-title":"Railway track circuit fault diagnosis using recurrent neural networks","volume":"28","author":"De Bruin","year":"2016","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.3233\/JCM-226883_ref19","doi-asserted-by":"crossref","unstructured":"Yuan M, Wu Y, Lin L, editors. Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. In: 2016 IEEE International Conference on Aircraft Utility Systems (AUS). IEEE; 2016.","DOI":"10.1109\/AUS.2016.7748035"},{"issue":"8","key":"10.3233\/JCM-226883_ref20","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"10.3233\/JCM-226883_ref21","doi-asserted-by":"crossref","first-page":"7675","DOI":"10.1109\/ACCESS.2017.2785763","article-title":"Data-based line trip fault prediction in power systems using LSTM networks and SVM","volume":"6","author":"Zhang","year":"2017","journal-title":"Ieee Access"},{"key":"10.3233\/JCM-226883_ref22","doi-asserted-by":"crossref","unstructured":"Xayalath C, Premrudeepreechacharn S, Ngamsanroaj K, editors. Detection Measurement Equipment Fault in Power distribution Using Long Short-Term Memory on Automatic Meter Reading. In: 2022 19th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE; 2022.","DOI":"10.1109\/ECTI-CON54298.2022.9795615"},{"issue":"13","key":"10.3233\/JCM-226883_ref23","doi-asserted-by":"crossref","first-page":"4751","DOI":"10.3390\/en15134751","article-title":"Borderline SMOTE Algorithm and Feature Selection-Based Network Anomalies Detection Strategy","volume":"15","author":"Sun","year":"2022","journal-title":"Energies"},{"key":"10.3233\/JCM-226883_ref24","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J, editors. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"10.3233\/JCM-226883_ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1198\/10618600152418584","article-title":"The art of data augmentation","volume":"10","author":"Van Dyk","year":"2001","journal-title":"Journal of Computational and Graphical Statistics"},{"issue":"8","key":"10.3233\/JCM-226883_ref26","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural computation"},{"key":"10.3233\/JCM-226883_ref27","doi-asserted-by":"crossref","first-page":"102119","DOI":"10.1109\/ACCESS.2019.2931500","article-title":"Automatic cardiac arrhythmia classification using combination of deep residual network and bidirectional LSTM","volume":"7","author":"He","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JCM-226883_ref28","unstructured":"Santurkar S, Tsipras D, Ilyas A, Madry A. How does batch normalization help optimization? Advances In Neural Information Processing Systems. 2018; 31."},{"key":"10.3233\/JCM-226883_ref29","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Advances in Neural Information Processing Systems. 2017; 30."},{"key":"10.3233\/JCM-226883_ref30","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1017\/S0962492900002919","article-title":"Approximation theory of the MLP model in neural networks","volume":"8","author":"Pinkus","year":"1999","journal-title":"Acta Numerica"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-226883","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:07:04Z","timestamp":1776809224000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-226883"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":30,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jcm-226883","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,6]]}}}