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首页> 外文期刊>Energies >A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression
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A Novel Remaining Useful Life Prediction Approach for Superbuck Converter Circuits Based on Modified Grey Wolf Optimizer-Support Vector Regression

机译：基于改进的灰狼优化器-支持向量回归的Superbuck变换器电路剩余使用寿命预测方法

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摘要

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.
机译：电源板的可靠性对于电子设备的性能非常重要，确保电力电子电路的可靠性对于设备安全尤为重要。转换器组件参数的更改可能会导致电源质量下降。为了有效地防止故障并估计超级降压转换器的剩余使用寿命（RUL），本文提出了一种电路故障预测框架。我们使用电路输出电压的平均值和纹波值作为特征集来计算马哈拉诺比斯距离（MD），以反映电路的健康状态。时变MD集形成电路状态时间序列。根据获得的工作条件时间序列，我们可以使用支持向量回归（SVR）预测以后的情况。在估算RUL之前，已通过改进的灰狼优化器（MGWO）算法对SVR进行了改进。这是将灰狼优化器（GWO）的修改版本应用于电路预测和系统健康管理（PHM）的首次尝试。随后，基准功能已用于验证MGWO的性能。最后，对比实验的仿真结果表明，MGWO-SVR能够以较小的误差和较高的预测精度预测电路的RUL。

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