NDLI: Feature extraction and classification method for switchgear faults based on sample entropy and cloud model

Content Provider	IET Digital Library
Author	Cui, Jindong Zheng, Que Xin, Yechun Zhou, Chuan Wang, Qianggang Zhou, Niancheng
Abstract	This study proposes a fault diagnosis method for extracting and classifying the fault features of switchgears in accordance with the monitoring data of three-phase voltage and current, temperature, humidity, and flashing from the smart breaker and sensors. The fault features are calculated using multivariate multiscale sample entropy (MMSE) for the data mining of multivariate monitoring process. The similarity measure of the composite delay vectors of multivariate time series in MMSE is improved by introducing a cloud model to soften the similar tolerance criterion. The modified MMSE is defined as multivariate multiscale cloud sample entropy (MMCSE). The MMCSE features of switchgear monitoring data can be achieved in different time scales in describing various switchgear faults. Subsequently, a classification method based on fuzzy support vector machine (FSVM) is further adopted to identify different types of switchgear faults using the MMCSE features. In addition, a dropping semi-normal membership cloud model is applied to modify the uncertainty quantification of the relationship among fault samples in FSVM. The effectiveness of the proposed method is validated with the monitoring data in a 10 kV switchboard.
Starting Page	2938
Ending Page	2946
Page Count	9
ISSN	17518687
Volume Number	11
e-ISSN	17518695
Issue Number	Issue 11, Aug (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/11/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2016.1459
Journal	IET Generation, Transmission & Distribution
Publisher Date	2017-06-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics Composite Delay Vector Data Handling Technique Data Mining Dropping Seminormal Membership Cloud Model Entropy Fault Diagnosis Fault Diagnosis Method Fault Feature Classification Method Feature Extraction FSVM Fuzzy Set Theory Fuzzy Support Vector Machine Knowledge Engineering Technique MMCSE Feature MMSE Multivariate Monitoring Process Multivariate Multiscale Cloud Sample Entropy Multivariate Multiscale Sample Entropy Multivariate Time Series Power Engineering Computing Sample Entropy Sensor Signal Classification Signal Processing And Detection Signal Processing Theory Smart Breaker Statistics Support Vector Machine Switch Switchgear Fault Switchgear Monitoring Data Three-phase Voltage Time Series Tolerance Criterion Uncertainty Quantification
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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