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Energies 2017, 10(4), 459; doi:10.3390/en10040459

A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression

1
College of Electronic and Information Engineering, Tongji University, No. 4800, Cao’an Highway, Shanghai 201804, China
2
School of Ocean and Earth Science, Tongji University, No. 1239, Siping Road, Shanghai 200092, China
*
Author to whom correspondence should be addressed.
Academic Editors: Laura Ramirez Elizondo and Frede Blaabjerg
Received: 9 January 2017 / Revised: 19 March 2017 / Accepted: 20 March 2017 / Published: 2 April 2017
(This article belongs to the Special Issue DC Systems)
View Full-Text   |   Download PDF [4541 KB, uploaded 2 April 2017]   |  

Abstract

The reliability of power packs is very important for the performance of electronic equipment and ensuring the reliability of power electronic circuits is especially vital for equipment security. An alteration in the converter component parameter can lead to the decline of the power supply quality. In order to effectively prevent failure and estimate the remaining useful life (RUL) of superbuck converters, a circuit failure prognostics framework is proposed in this paper. We employ the average value and ripple value of circuit output voltage as a feature set to calculate the Mahalanobis distance (MD) in order to reflect the health status of the circuit. Time varying MD sets form the circuit state time series. According to the working condition time series that have been obtained, we can predict the later situation with support vector regression (SVR). SVR has been improved by a modified grey wolf optimizer (MGWO) algorithm before estimating the RUL. This is the first attempt to apply the modified version of the grey wolf optimizer (GWO) to circuit prognostics and system health management (PHM). Subsequently, benchmark functions have been used to validate the performance of the MGWO. Finally, the simulation results of comparative experiments demonstrate that MGWO-SVR can predict the RUL of circuits with smaller error and higher prediction precision. View Full-Text
Keywords: superbuck converter circuit; prognostics and system health management (PHM); remaining useful life (RUL) prediction; support vector regression (SVR); modified grey wolf optimizer (MGWO) superbuck converter circuit; prognostics and system health management (PHM); remaining useful life (RUL) prediction; support vector regression (SVR); modified grey wolf optimizer (MGWO)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wang, L.; Yue, J.; Su, Y.; Lu, F.; Sun, Q. A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression. Energies 2017, 10, 459.

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