Measurement Error Prediction of Power Metering Equipment Using Improved Local Outlier Factor and Kernel Support Vector Regression

The measurement error evaluation of power metering equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially under extreme environmental stresses. However, actual measurement error assessment is often disturbed by the environmental noise and insuff...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 69; no. 9; pp. 9575 - 9585
Main Authors: Ma, Jun, Teng, Zhaosheng, Tang, Qiu, Qiu, Wei, Yang, Yingying, Duan, Junfeng
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Background noise
Companies
Data analysis
Degradation
Error analysis
Extreme environmental stresses
improved kernel support vector regression (ILOF)
Kernel
kernel support vector regression (KSVR)
Kernels
Measurement
measurement error assessment
Measurement errors
Metering
Outliers (statistics)
power metering equipment (PME)
Reliability
Stress
Stresses
Support vector machines
Temperature measurement
ISSN:0278-0046, 1557-9948
Online Access:Get full text
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Summary:The measurement error evaluation of power metering equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially under extreme environmental stresses. However, actual measurement error assessment is often disturbed by the environmental noise and insufficient input information. To address this problem, an improved local outlier factor (ILOF) method is first presented to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on box plot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely kernel support vector regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3114740