{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:41:13Z","timestamp":1760150473589,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Key Program of National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2066203","61973178","52377117","BE2021063"],"award-info":[{"award-number":["U2066203","61973178","52377117","BE2021063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2066203","61973178","52377117","BE2021063"],"award-info":[{"award-number":["U2066203","61973178","52377117","BE2021063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Plan of Jiangsu Province","award":["U2066203","61973178","52377117","BE2021063"],"award-info":[{"award-number":["U2066203","61973178","52377117","BE2021063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Supervisory control and data acquisition (SCADA) systems are widely utilized in power equipment for condition monitoring. For the collected data, there generally exists a problem\u2014missing data of different types and patterns. This leads to the poor quality and utilization difficulties of the collected data. To address this problem, this paper customizes methodology that combines an asymmetric denoising autoencoder (ADAE) and moving average filter (MAF) to perform accurate missing data imputation. First, convolution and gated recurrent unit (GRU) are applied to the encoder of the ADAE, while the decoder still utilizes the fully connected layers to form an asymmetric network structure. The ADAE extracts the local periodic and temporal features from monitoring data and then decodes the features to realize the imputation of the multi-type missing. On this basis, according to the continuity of power data in the time domain, the MAF is utilized to fuse the prior knowledge of the neighborhood of missing data to secondarily optimize the imputed data. Case studies reveal that the developed method achieves greater accuracy compared to existing models. This paper adopts experiments under different scenarios to justify that the MAF-ADAE method applies to actual power equipment monitoring data imputation.<\/jats:p>","DOI":"10.3390\/s23249697","type":"journal-article","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T03:03:33Z","timestamp":1702004613000},"page":"9697","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter\u2013Asymmetric Denoising Autoencoder"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-3342","authenticated-orcid":false,"given":"Ling","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0078-4602","authenticated-orcid":false,"given":"Juping","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Nantong University, Nantong 226019, China"},{"name":"School of Electrical and Information Engineering, Suzhou University of Science and Technology, Suzhou 215101, China"}]},{"given":"Xinsong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Nantong University, Nantong 226019, China"}]},{"given":"Liang","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Nantong University, Nantong 226019, China"}]},{"given":"Yueming","family":"Cai","sequence":"additional","affiliation":[{"name":"NARI Technology Company Limited, NARI Group Corporation, Nanjing 211106, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/TSG.2020.3028501","article-title":"Self-Assessment of Health Conditions of Electrical Assets and Grid Components: A Contribution to Smart Grids","volume":"12","author":"Montanari","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1109\/TSG.2019.2960043","article-title":"Synchrophasor-based condition monitoring of instrument transformers using clustering approach","volume":"11","author":"Cui","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1109\/TSG.2016.2580002","article-title":"Impact of GPS signal loss and its mitigation in power system synchronized measurement devices","volume":"9","author":"Yao","year":"2016","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TIM.2015.2485339","article-title":"Noise suppression of corona current measurement from HVdc transmission lines","volume":"65","author":"Wang","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1109\/TDEI.2015.005532","article-title":"Denoising of acoustic partial discharge signals corrupted with random noise","volume":"23","author":"Hussein","year":"2016","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1093\/ije\/dyu080","article-title":"What is the difference between missing completely at random and missing at random?","volume":"43","author":"Bhaskaran","year":"2014","journal-title":"Int. J. Epidemiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"964","DOI":"10.35833\/MPCE.2020.000894","article-title":"Data-driven missing data imputation for wind farms using context encoder","volume":"10","author":"Liao","year":"2021","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"ref_8","unstructured":"Wan, C., Chen, H., Guo, M., and Liang, Z. (2016, January 25\u201328). Wrong data identification and correction for WAMS. Proceedings of the 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference, Xi\u2019an, China."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s40565-016-0213-8","article-title":"Data quality issues for synchrophasor applications Part II: Problem formulation and potential solutions","volume":"4","author":"Huang","year":"2016","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1109\/TPWRS.2015.2413935","article-title":"Missing data recovery by exploiting Low-dimensionality in power system synchrophasor measurements","volume":"31","author":"Gao","year":"2015","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1109\/TPAMI.2012.271","article-title":"Fast and accurate matrix completion via truncated nuclear norm regularization","volume":"35","author":"Hu","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4554","DOI":"10.1109\/TSG.2018.2864176","article-title":"An Alternating Direction Method of Multipliers Based Approach for PMU Data Recovery","volume":"10","author":"Liao","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4321","DOI":"10.1109\/TSG.2020.2986439","article-title":"Synchrophasor missing data recovery via data-driven filtering","volume":"11","author":"Konstantinopoulos","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1109\/TPWRS.2014.2347047","article-title":"Methodology for performing synchrophasor data conditioning and validation","volume":"30","author":"Jones","year":"2014","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102673","DOI":"10.1016\/j.trc.2020.102673","article-title":"A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation","volume":"117","author":"Chen","year":"2020","journal-title":"Transp. Res. Part C-Emerg. Technol."},{"key":"ref_16","first-page":"3732","article-title":"Delay aware power system synchrophasor recovery and prediction framework","volume":"10","author":"James","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"117655","DOI":"10.1016\/j.apenergy.2021.117655","article-title":"Missing data imputation using mixture factor analysis for building electric load data","volume":"304","author":"Jeong","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jung, S., Moon, J., Park, S., Rho, S., Baik, S.W., and Hwang, E. (2020). Bagging ensemble of multilayer perceptrons for missing electricity consumption data imputation. Sensors, 20.","DOI":"10.3390\/s20061772"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5044","DOI":"10.1109\/TPWRS.2019.2922671","article-title":"A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data","volume":"34","author":"Ren","year":"2019","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"22863","DOI":"10.1109\/ACCESS.2017.2740968","article-title":"Cleaning method for status monitoring data of power equipment based on stacked denoising autoencoders","volume":"5","author":"Dai","year":"2017","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"119292","DOI":"10.1016\/j.apenergy.2022.119292","article-title":"Data cleaning and restoring method for vehicle battery big data platform","volume":"320","author":"Li","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_22","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pintelas, E., Livieris, I.E., and Pintelas, P.E. (2021). A convolutional autoencoder topology for classification in high-dimensional noisy image datasets. Sensors, 21.","DOI":"10.3390\/s21227731"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13902","DOI":"10.1109\/TCYB.2021.3121312","article-title":"An accurate GRU-based power time-series prediction approach with selective state updating and stochastic optimization","volume":"52","author":"Zheng","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F., and Li, X. (2021, January 11\u201317). Fcanet: Frequency channel attention networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107244","DOI":"10.1109\/ACCESS.2020.3000557","article-title":"Kick: Shift-N-Overlap cascades of transposed convolutional layer for better autoencoding reconstruction on remote sensing imagery","volume":"8","author":"Hong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13198","DOI":"10.1109\/ACCESS.2021.3052142","article-title":"Moving Regression Filtering with Battery State of Charge Feedback Control for Solar PV Firming and Ramp Rate Curtailment","volume":"9","author":"Syed","year":"2021","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101900","DOI":"10.1016\/j.scs.2019.101900","article-title":"Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range","volume":"53","author":"Meng","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_29","unstructured":"(2023, December 01). Irish Electricity Energy System Monitoring Data. Available online: https:\/\/smartgriddashboard.com."},{"key":"ref_30","unstructured":"(2023, December 01). Australian Electricity Load Data. Available online: https:\/\/www.aemo.com.au\/energy-systems\/electricity\/national-electricity-market-nem\/data-nem."},{"key":"ref_31","unstructured":"Yu, H., Rao, N., and Dhillon, I.S. (2016, January 5\u201310). Temporal regularized matrix factorization for high-dimensional time series prediction. Proceedings of the International Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109903","DOI":"10.1016\/j.asoc.2022.109903","article-title":"Anomaly detection of power battery pack using gated recurrent units based variational autoencoder","volume":"132","author":"Sun","year":"2023","journal-title":"Appl. Soft. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9697\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:35:11Z","timestamp":1760132111000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9697"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,8]]},"references-count":32,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23249697"],"URL":"https:\/\/doi.org\/10.3390\/s23249697","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,12,8]]}}}