Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter–Asymmetric Denoising Autoencoder

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—missing data of different types and patterns. This leads to the poor quality and utilization difficulties of the collecte...

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Published in:	Sensors (Basel, Switzerland) Vol. 23; no. 24; p. 9697
Main Authors:	Jiang, Ling, Gu, Juping, Zhang, Xinsong, Hua, Liang, Cai, Yueming
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 08.12.2023 MDPI
Subjects:	Accuracy asymmetric denoising autoencoder Case studies Data entry data imputation Data transmission Deep learning Missing data moving average filter multi-type missing power equipment monitoring data Sensors Teaching methods moving average filter data imputation asymmetric denoising autoencoder power equipment monitoring data multi-type missing
ISSN:	1424-8220, 1424-8220
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Abstract	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—missing 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.
AbstractList	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-missing 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. 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-missing 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.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-missing 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.
Audience	Academic
Author	Zhang, Xinsong Hua, Liang Gu, Juping Jiang, Ling Cai, Yueming
AuthorAffiliation	2 School of Electrical and Information Engineering, Suzhou University of Science and Technology, Suzhou 215101, China 1 School of Information Science and Technology, Nantong University, Nantong 226019, China; 2010510006@stmail.ntu.edu.cn 3 School of Electrical Engineering, Nantong University, Nantong 226019, China; zhang.xs@ntu.edu.cn (X.Z.); hualiang@ntu.edu.cn (L.H.) 4 NARI Technology Company Limited, NARI Group Corporation, Nanjing 211106, China; 13951969176@163.com
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SubjectTerms	Accuracy asymmetric denoising autoencoder Case studies Data entry data imputation Data transmission Deep learning Missing data moving average filter multi-type missing power equipment monitoring data Sensors Teaching methods
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Title	Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter–Asymmetric Denoising Autoencoder
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