中国电力 ›› 2022, Vol. 55 ›› Issue (7): 1-10.DOI: 10.11930/j.issn.1004-9649.202201008

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基于改进支持向量回归的IGBT老化预测

陈正雄, 帕孜来·马合木提, 沈玮   

  1. 新疆大学 电气工程学院,新疆 乌鲁木齐 830017
  • 收稿日期:2022-01-02 修回日期:2022-05-07 出版日期:2022-07-28 发布日期:2022-07-20
  • 作者简介:陈正雄(1995—),男,硕士研究生,从事复杂系统故障诊断研究,E-mail:chenzhengxiong@163.com;帕孜来·马合木提(1965—),女,通信作者,教授,从事过程控制与智能故障诊断研究,E-mail:987654@qq.com;沈玮(1996—),男,硕士研究生,从事微电网故障诊断研究,E-mail:188295851@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61963034)

Aging Prediction of IGBT Based on Improved Support Vector Regression

CHEN Zhengxiong, PAZILAI Mahemuti, SHEN Wei   

  1. School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
  • Received:2022-01-02 Revised:2022-05-07 Online:2022-07-28 Published:2022-07-20
  • Supported by:
    This work is supported by National Natural Science Foundation of China (No.61963034)

摘要: 为了准确预测绝缘栅双极型晶体管(IGBT)的老化状态,提出了一种基于改进鲸鱼优化算法(IWOA)优化支持向量回归(SVR)的IGBT老化预测方法。该方法提取IGBT集电极-发射极电压信号的时频域特征,通过核主成分分析(KPCA)降维将时频域特征融合成一个综合指标来表征IGBT的老化状态;针对鲸鱼优化算法(WOA)不足,在WOA的基础上引入Sobol序列种群初始化、惯性权重和反向学习策略,增强WOA的局部搜索能力和收敛速度;利用IWOA优化SVR的惩罚因子和核参数,并构建一种基于综合指标的IGBT预测模型。利用NASA Ames实验室的IGBT老化数据集对IWOA-SVR方法进行验证,结果表明,所构建IWOA-SVR预测模型可以更准确实现对IGBT的老化预测。

关键词: 绝缘栅双极型晶体管, 老化预测, 支持向量回归, 鲸鱼优化算法, 核主成分分析

Abstract: In order to accurately predict the aging state of insulated gate bipolar transistor (IGBT), a novel IGBT aging prediction method is proposed based on improved whale optimization algorithm (IWOA) and optimized support vector regression (SVR). In this method, the time-frequency domain characteristics of IGBT collector-emitter voltage signals are extracted and are integrated into a comprehensive index to characterize the aging state of IGBT through dimension reduction of kernel principal component analysis (KPCA); Against the deficiency of the whale optimization algorithm (WOA), the Sobol sequence population initialization, inertia weight and reverse learning strategy are introduced to enhance the local search ability and convergence speed of WOA; The IWOA is used to optimize the penalty factor and kernel parameters of SVR, and an IGBT prediction model is constructed based on the comprehensive index. The proposed IWOA-SVR method is verified by using the IGBT aging data set of the NASA Ames laboratory. The results show that the constructed IWOA-SVR prediction model can more accurately predict the aging of IGBT.

Key words: insulated gate bipolar transistor, aging prediction, support vector regression, whale optimization algorithm, nuclear principal component analysis