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

Author

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  • Li Wang

    (College of Electronic and Information Engineering, Tongji University, No. 4800, Cao’an Highway, Shanghai 201804, China)

  • Jiguang Yue

    (College of Electronic and Information Engineering, Tongji University, No. 4800, Cao’an Highway, Shanghai 201804, China)

  • Yongqing Su

    (College of Electronic and Information Engineering, Tongji University, No. 4800, Cao’an Highway, Shanghai 201804, China)

  • Feng Lu

    (School of Ocean and Earth Science, Tongji University, No. 1239, Siping Road, Shanghai 200092, China)

  • Qiang Sun

    (College of Electronic and Information Engineering, Tongji University, No. 4800, Cao’an Highway, Shanghai 201804, China)

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.

Suggested Citation

  • Li Wang & Jiguang Yue & Yongqing Su & Feng Lu & Qiang Sun, 2017. "A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression," Energies, MDPI, vol. 10(4), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:459-:d:94830
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    References listed on IDEAS

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    1. Zhaoxuan Li & SM Mahbobur Rahman & Rolando Vega & Bing Dong, 2016. "A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting," Energies, MDPI, vol. 9(1), pages 1-12, January.
    2. Cheng-Ming Lee & Chia-Nan Ko, 2016. "Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network," Energies, MDPI, vol. 9(12), pages 1-15, November.
    3. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
    4. Li-Ling Peng & Guo-Feng Fan & Min-Liang Huang & Wei-Chiang Hong, 2016. "Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting," Energies, MDPI, vol. 9(3), pages 1-20, March.
    5. Qian Zhang & Kin Keung Lai & Dongxiao Niu & Qiang Wang & Xuebin Zhang, 2012. "A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power," Energies, MDPI, vol. 5(9), pages 1-18, September.
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