一种无刷同步发电机旋转整流器故障快速识别方法

doi:10.3969/j.issn.1006-2475.2017.10.014

计算机与现代化

一种无刷同步发电机旋转整流器故障快速识别方法

南京航空航天大学自动化学院,江苏南京211106

收稿日期:2017-01-18 出版日期:2017-10-30 发布日期:2017-10-31
作者简介:唐军祥(1993-),男，安徽芜湖人,南京航空航天大学自动化学院硕士研究生,研究方向:发电机故障诊断与健康监测；崔江(1977-),男,副教授,博士,研究方向:模拟电路测试和故障预测。
基金资助:
中央高校基本科研业务费专项资金资助(NS2017019)

A Fast Fault Recognition Method of Brushless Synchronous Generator Rotating Rectifier

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received:2017-01-18 Online:2017-10-30 Published:2017-10-31

摘要/Abstract

摘要： 针对目前无刷同步发电机旋转整流器故障识别方法识别速度慢的问题，提出一种基于改进极限学习机的故障快速识别技术。该方法通过鸡群算法优化极限学习机的参数，得到优化的识别模型，并将其应用于无刷同步发电机旋转整流器的故障识别中。实验结果表明，经过优化的极限学习机与现有分类方法相比，具有很好的诊断性能和较高的分类速度。该方法适用于无刷同步发电机旋转整流器故障快速识别和定位。

关键词: 无刷同步发电机, 旋转整流器, 故障识别, 鸡群算法, 极限学习机

Abstract: Focusing on the slow speed problem of existing brushless synchronous generator rotating rectifier fault recognition methods, this paper presents a fast recognition technique based on improved extreme learning machine (ELM). The chicken swarm optimization (CSO) is used to optimize the parameters of ELM, and hence, an optimized model of ELM can be achieved, and then applied it to rotating rectifier faults recognition of brushless synchronous generator. Experimental results show that, the optimized ELM can achieve good diagnosis performance and high classification speed. The presented method can be considered to the application of brushless synchronous generator rotating rectifier faults recognition and localization.

Key words: brushless synchronous generator, rotating rectifier, fault recognition, chicken swarm optimization, extreme learning machine

唐军祥,崔江. 一种无刷同步发电机旋转整流器故障快速识别方法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2017.10.014.

TANG Jun-xiang, CUI Jiang. A Fast Fault Recognition Method of Brushless Synchronous Generator Rotating Rectifier[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.10.014.

参考文献

［1］严仰光,秦海鸿,龚春英,等. 多电飞机与电力电子［J］. 南京航空航天大学学报, 2014,46(1):11-18.

［2］ Mcardle M G, Morrow D J. Noninvasive detection of brushless exciter rotating diode failure［J］. IEEE Transactions on Energy Conversion, 2004,19(2):378-383. 

［3］ Batzel T D, Swanson D C, Defenbaugh J F. Predictive diagnostics for the main field winding and rotating rectifier assembly in the brushless synchronous generator ［C］// Proceedings of the 4th IEEE International Symposium on Diagnostics for Electric Machines，Power Electronics and Drives. 2003:349-354.

［4］ Salah M, Bacha K, Chaari A, et al. Brushless three-phase synchronous generator under rotating diode failure conditions［J］. IEEE Transactions on Energy Conversion, 2014,29(3):594-601.

［5］ Kappaganthu K, Li Yuan, Hernandez L． Model based diagnostics of an aircraft generator using AAKR and SPRT［J］. SAE International Journal of Aerospace, 2010,3(1):137-143.

［6］ Hang Jun, Ding Shichuan, Li Guoli, et al. Fault diagnosis of a permanent magnet synchronous generator connected to a uncontrolled rectifier bridge［C］// IEEE the 11th Conference on Industrial Electronics and Applications. 2016:2015-2020．

［7］ Liu Nian, Liu Ying, Xie Chi. Fuzzy recognition processing of complex fault signals from the rotating electric circuit in synchronous generators［J］. International Journal of Electrical Power & Energy Systems, 2013,49(1):354-358.

［8］ Tang Xiaojun, Fan Dasen, Liu Liang, et al．Fault signal analysis for aircraft generator rectifier［C］// Proceedings of the 1st Symposium on Aviation Maintenance and Management. 2014,1:569-578．

［9］张敬,李颖晖,朱喜华,等. 三级式发电机旋转整流器故障特征提取［J］. 微电机, 2011 44(7):92-96．

［10］刘海港,董芷岑. 航空发电机综合故障诊断技术研究［J］. 电子测量技术, 2014,37(3):125-127.

［11］和麟,梁丽嫒,黄潇瑶,等. 基于粒子群优化SVM的飞机发电机故障诊断［J］. 计算机测量与控制, 2013,21(12)：3237-3239.

［12］Zhang Jingnan, Wu Liqin, Zhang Tianhao． Fault diagnosis of stator winding turn to turn short circuit of synchronous rectifier generator［C］// Proceedings of 2016 IEEE International Conference on Mechatronics and Automation. 2016:2001-2005.

［13］Huang Guangbin, Zhu Qinyu, Siew C K. Extreme learning machine:Theory and applications［J］. Neurocomputing, 2006,70(1):489-501．

［14］Minhas R, Mohammed A A, Wu Q M J． A fast recognition framework based on extreme learning machine using hybrid object information［J］. Neurocomputing, 2010,73(10-12):1831-1839.

［15］Meng Xianbing, Liu Yu, Gao Xiaozhi, et al．A new bio-inspired algorithm: Chicken swarm optimization［C］// International Conference in Swarm Intelligence. 2014:86-94．

［16］Suresh S, Saraswathi S, Sundararajan N. Performance enhancement of extreme learning machine for multi-category sparse cancer classification［J］. Engineering Applications of Artificial Intelligence, 2010,23(7):1149-1157.

［17］秦海鸿,严仰光. 多电飞机的电气系统［M］．北京：北京航空航天大学出版社, 2015:80-81.

［18］Dinesh G, Adithya B V, Vedula V． Modeling of a traditional aircraft generator and its sub-systems［C］// IEEE International Conference on Electrical, Computer and Communication Technologies. 2015:1-7.

［19］张超,夏立. 基于谐波分析的旋转整流器故障检测［J］. 电机与控制应用, 2008,35(11):51-54.

［20］Liu Jianying, Gao Kai, Liu Pengfei. Design and application for fault monitoring circuit of rotating rectifier in aviation brushless AC generator［C］// Proceedings of 2014 IEEE Chinese Guidance，Navigation and Control Conference. 2014:1235-1239.

[1]	曾鼎, 曾勇. 基于改进极限学习机的泥石流发生预测[J]. 计算机与现代化, 2020, 0(09): 95-99.
[2]	徐肖遥, 张鹏飞, 蒋剑. 基于EMD-GAELM-ARIMA算法的大坝变形预测[J]. 计算机与现代化, 2020, 0(07): 1-5.
[3]	李思雨 . 基于极限学习机的组合软件可靠性模型研究[J]. 计算机与现代化, 2019, 0(11): 44-.
[4]	席晓燕,于化龙. #br# 耦合样本先验分布信息的加权极限学习机[J]. 计算机与现代化, 2018, 0(08): 61-.
[5]	董雪，张德平. 基于极限学习机的武器装备作战效能全局敏感性分析[J]. 计算机与现代化, 2018, 0(05): 86-.
[6]	廖洪一，王欣. 基于极限学习机的股票价格预测[J]. 计算机与现代化, 2014, 0(12): 19-22.

一种无刷同步发电机旋转整流器故障快速识别方法

A Fast Fault Recognition Method of Brushless Synchronous Generator Rotating Rectifier

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

本文评价