电磁发射系统监测量预测方法

doi:10.19595/j.cnki.1000-6753.tces.171363

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (40327 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The numerical prediction of the main state of the equipment is one of the most significant parts of prognostic and health management (PHM) study. Taken the stator temperature of a segment - powered linear motor in an electromagnetic launch system as an example, the multiple time scales prediction of stator temperature is achieved respectively based on the autoregressive integrated moving average (ARIMA) model, Kalman filtering model, back propagation (BP) neural network model and a new nonlinear autoregressive neural network (NARX) model with external input of working conditions. The ARIMA model provides the basis for determining the number of orders in time series data analysis for the other three models. With the test data different from training data, the four methods are compared and the multiple time scales prediction results are obtained. For short-term temperature predictions up to 1 minute, all four methods have better effects; for moderate to long-term predictions from 1 minute to 4 minutes, the NARX neural network method with the working condition information has advantages; all four methods do not have the predictive ability of more than 4 minutes for the stator temperature prediction of the segment - powered linear motor.

Key words： Electromagnetic emission system segment-powered linear motor prediction of monitoring quantities nonlinear autoregressive neural network (NARX) working condition information

Received: 25 September 2017 Published: 27 November 2018

PACS:

TM76

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Teng Teng
	Zhao Zhihua

Cite this article:

Teng Teng,Zhao Zhihua. The Prediction Method of Monitoring Quantities of Electromagnetic Emission System[J]. Transactions of China Electrotechnical Society, 2018, 33(22): 5233-5243.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.171363 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I22/5233

[1] 马伟明, 肖飞, 聂世雄. 电磁发射系统中电力电子技术的应用与发展[J]. 电工技术学报, 2016, 31(19): 1-10.
Ma Weiming, Xiao Fei, Nie Shixiong.Applications and development of power electronics in elec- tromagnetic launch system[J]. Transactions of China Electrotechnical Society, 2016, 31(19): 1-10.
[2] Gu Jie, Lau D, Pecht M.Health assessment and prognostics of electronic products[C]//International Conference on Reliability, Chengdu, China, 2009: 912-919.
[3] Edward R B, Mccollom N N, Erin-Elaine M, et al.Prognostics and health management a data-driven approach to supporting the F-35 lightning II[C]// Aerospace Conference, Big Sky, MT, USA, 2007: 1-12.
[4] Lebold M, Reichard K, Byington C S, et al.OSA-CBM architecture development with emphasis on XML implementations[C]//Proceedings of the Maintenance and Reliability Conference (MARCON 2002), Knoxville, TN, 2002: 1-16.
[5] 彭宇, 刘大同. 数据驱动故障预测和健康管理综述[J]. 仪器仪表学报, 2014, 35(3): 481-495.
Peng Yu, Liu Datong.Data-driven prognostics and health management: a review of recent advances[J]. Chinese Journal of Scientific Instrument, 2014, 35(3): 481-495.
[6] 崔小鹏, 王公宝, 马伟明, 等. 直线电机分段供电故障诊断研究[J]. 电机与控制学报, 2013, 17(8): 63-70.
Cui Xiaopeng, Wang Gongbao, Ma Weiming, et al.Research on fault diagnosis of segment-powered linear induction motor[J]. Electric Machines and Control, 2013, 17(8): 63-70.
[7] 田波, 朴在林, 郭丹, 等. 基于改进EEMD_SE_ ARMA的超短期风功率组合预测模型[J]. 电力系统保护与控制, 2017, 45(4): 72-79.
Tian Bo, Piao Zailin, Guo Dan, et al.Wind power ultra short-term model based on improved EEMD- SE-ARMA[J]. Power System Protection and Control, 2017, 45(4): 72-79.
[8] Erdem E, Shi Jing.ARMA based approaches for forecasting the tuple of wind speed and direction[J]. Applied Energy, 2011, 88(4): 1405-1414.
[9] 龚莺飞, 鲁宗相, 乔颖, 等. 光伏功率预测技术[J]. 电力系统自动化, 2016, 40(4): 140-151.
Gong Yingfei, Lu Zongxiang, Qiao Ying, et al.An overview of photovoltaic energy system output forecasting technology[J]. Automation of Electric Power Systems, 2016, 40(4): 140-151.
[10] 王丽婕, 冬雷, 高爽. 基于多位置NWP与主成分分析的风电功率短期预测[J]. 电工技术学报, 2015, 30(5): 79-84.
Wang Lijie, Dong Lei, Gao Shuang.Wind power short-term prediction based on principal component analysis of NWP of multiple locations[J]. Transa- ctions of China Electrotechnical Society, 2015, 30(5): 79-84.
[11] Louka P, Galanis G, Siebert N, et al.Improvements in wind speed forecasts for wind power prediction purposes using kalman filtering[J]. Journal of Wind Engineering & Industrial Aerodynamics, 2008, 96(12): 2348-2362.
[12] 修春波, 任晓, 李艳晴, 等. 基于卡尔曼滤波的风速序列短期预测方法[J]. 电工技术学报, 2014, 29(2): 253-259.
Xiu Chunbo, Ren Xiao, Li Yanqing, et al.Short-term prediction method of wind speed series based on Kalman filtering fusion[J]. Transactions of China Electrotechnical Society, 2014, 29(2): 253-259.
[13] 任亮, 赵兴勇. 基于相似性算法的超短期风速预测[J]. 电力学报, 2016, 31(1): 36-40,46.
Ren Liang, Zhao Xingyong.Ultra-short term wind speed prediction based on similarity algorithm[J]. Journal of Electric Power, 2016, 31(1): 36-40, 46.
[14] 李龙, 魏靖, 黎灿兵, 等. 基于人工神经网络的负荷模型预测[J]. 电工技术学报, 2015, 30(8): 226-230.
Li Long, Wei Jing, Li Canbing, et al.Prediction of load model based on artificial neural network[J]. Transactions of China Electrotechnical Society, 2015, 30(8): 226-230.
[15] 王新普, 周想凌, 邢杰, 等. 一种基于改进灰色BP神经网络组合的光伏出力预测方法[J]. 电力系统保护与控制, 2016, 44(18): 81-87.
Wang Xinpu, Zhou Xiangling, Xing Jie, et al.A prediction method of PV output power based on the combination of improved grey back propagation neural network[J]. Power System Protection and Control, 2016, 44(18): 81-87.
[16] 张颖超, 王雅晨, 邓华, 等. 基于IAFSA_BPNN的短期风电功率预测[J]. 电力系统保护与控制, 2017, 45(7): 58-63.
Zhang Yingchao, Wang Yachen, Deng Hua, et al.IAFSA-BPNN for wind power probabilistic foreca- sting[J]. Power System Protection and Control, 2017, 45(7): 58-63.
[17] 陈亚, 李萍. 基于神经网络的短期电力负荷预测仿真研究[J]. 电气技术, 2017, 18(1): 26-29.
Chen Ya, Li Ping.Research on simulation of short- term power load forecasting based on neural net- work[J]. Electrical Engineering, 2017, 18(1): 26-29.
[18] 田中大, 李树江, 王艳红, 等. 基于小波变换的风电场短期风速组合预测[J]. 电工技术学报, 2015, 30(9): 112-120.
Tian Zhongda, Li Shujiang, Wang Yanhong, et al.Short-term wind speed combined prediction for wind farms based on wavelet transform[J]. Transactions of China Electrotechnical Society, 2015, 30(9): 112-120.
[19] Hui Liu, Tian Hongqi, Li Yanfei.Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction[J]. Applied Energy, 2012, 98(1): 415-424.
[20] 潘迪夫, 刘辉, 李燕飞. 基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型[J]. 电网技术, 2008, 32(7): 82-86.
Pan Difu, Liu Hui, Li Yanfei.A wind speed forecasting optimization model for wind farms based on time series analysis and Kalman filter algorithm[J]. Power System Technology, 2008, 32(7): 82-86.
[21] 刘辉. 铁路沿线风信号智能预测算法研究[D]. 长沙: 中南大学, 2011.
[22] 潘迪夫, 刘辉, 李燕飞. 风电场风速短期多步预测改进算法[J]. 中国电机工程学报, 2008, 28(26): 87-91.
Pan Difu, Liu Hui, Li Yanfei.Optimization algorithm of short-term multi-step wind speed forecast[J]. Proceedings of the CSEE, 2008, 28(26): 87-91.
[23] Gong Li, Shi Jing.On comparing three artificial neural networks for wind speed forecasting[J]. Applied Energy, 2010, 87(7): 2313-2320.
[24] Welch G, Bishop G.An introduction to the Kalman filter[M]. Chapel Hill: University of North Carolina at Chapel Hill, 1995.