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    ASIA unversity > 管理學院 > 經營管理學系  > 期刊論文 >  Item 310904400/112396

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/112396

    Title: Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation
    Authors: Ling-Ling Li;Jin Sun;Ming-Lang Tseng;Zhi-Gang Li
    Contributors: 經營管理學系
    Date: 2019-04
    Issue Date: 2019-11-08 11:06:52 (UTC+8)
    Abstract: This study focuses on the aging evaluation of Insulated gate bipolar transistor (IGBT) modules to ensure their stability during operation. An aging degree evaluation model is proposed based on whale optimization algorithm optimized extreme learning machine (WOA-ELM) algorithm. This study is mainly concentrated on two aspects. One is to use WOA to optimize the input weights and hidden layer biases of ELM to improve its prediction performance. This study tested the performance of WOA-ELM on several benchmark datasets. The results show that the prediction performance of WOA-ELM is better than ELM, genetic algorithm optimized ELM, cuckoo search optimized ELM, and dandelion algorithm optimized ELM. The other is to measure the electrical and thermal characteristic data of IGBT module under different aging conditions by accelerated aging test. Based on the analysis of the experimental data under different aging degrees, a method for evaluating the aging degree of IGBT modules based on WOA-ELM is proposed. Simulation results based on experimental data show that WOA-ELM still has better accuracy and generalization performance than others. In summary, the WOA-ELM algorithm is applicable to the aging evaluation method of IGBT modules proposed in this study which has good practical value.
    Appears in Collections:[經營管理學系 ] 期刊論文

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