Fault Simulation Analysis of Cascaded Inverter Based on Combination Neural Network
赵旭 ZHAO Xu
（鹤壁职业技术学院，鹤壁 458030）
（Hebi Vocational and Technical College，Hebi 458030，China）
摘要： 本文利用级联式变频器仿真模型，设计了一种经过优化改进的组合神经网络诊断方法，改进了遗传算法优化的组合神经网络，能够及时、准确的提取级联式变频器断路状态时的故障特征并进行有效分析，仿真结果验证了改进的组合神经网络能够满足级联式变频器故障诊断的实时性、稳定性、高效性，达到了预期设定要求。
Abstract: In this paper, an optimized and improved combined neural network diagnosis method is designed using the simulation model of the cascaded frequency converter, and the combined neural network optimized by the genetic algorithm is improved, which can timely and accurately extract the open-circuit state of the cascaded frequency converter. The fault characteristics were analyzed effectively, and the simulation results verified that the improved combined neural network can meet the real-time, stability and high efficiency of the fault diagnosis of the cascaded inverter, and meet the expected setting requirements.
关键词： 组合神经网络；级联式变频器；故障诊断
Key words: combined neural network；cascaded frequency converter；fault diagnosis
中图分类号：TN773 文献标识码：A 文章编号：1006-4311（2022）21-137-03
DOI:10.3969/j.issn.1006-4311.2022.21.043.
文章出处：赵旭. 基于组合神经网络的级联式变频器故障仿真分析[J]. 价值工程,2022,41(21):137-139.
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