基于神经网络的逆变器的研究
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摘要
逆变器在当今社会具有很广泛的应用,随着电力电子技术的快速发展,大量高功率开关器件相继出现,为逆变器在各行各业的应用提供了保障。本文研究的逆变器主电路采用三相桥式结构。在控制策略方面,传统的正弦脉宽调制(SPWM)具有直流电压利用率较低的缺点,空间矢量脉宽调制(SVPWM)是一种优化的脉宽调制技术,能明显减小逆变器输出电流的谐波成分并能提高电压的利用率,已有取代传统SPWM的趋势,本文即采用电压空间矢量脉宽调制作为控制策略。
本文首先对电力电子技术及逆变器的相关知识作了简要的介绍。对空间电压矢量脉宽调制的工作原理做了深入的研究,并且在此基础上构建了基于SVPWM算法的逆变器仿真模型,得到了仿真结果。之后本文提出了基于神经网络的逆变器的构想,在计算逆变器三相导通时间时,运用了3层前馈网络,并采用Levenberg-Marquardt算法对神经网络进行训练。最后,在MATLAB/Simulink环境下,对系统进行了仿真研究,仿真结果表明基于神经网络的SVPWM控制方法是可行的,能达到预期的效果,同时通过合理的运用神经网络,可以提高PWM的频率,减小输出电压中的谐波含量。
The inverter is used widely in society. With the rapid development of power electronics technology, a large amount of high-frequency switching equipment are appeared which provide the guarantee for the inverter’s application in all kinds of fields. The inverter’s main circuit discussed in this paper adopts the three-phase bridge structure. In terms of controller strategy, the traditional method of SPWM has the setback which is lower electric source utilization ratio. SVPWM is a kind of optimized method, it can reduce the output current’s harmonic component and improve the electric source utilization ratio, having the trend of replacing the traditional SPWM method, this paper just adopts the SVPWM as the controller strategy.
This paper firstly introduces the power electronics technology and inverter’s corresponding knowledge, and the paper deeply researches the work’s principle of the SVPWM and establishes the inverter’s simulation model based on the arithmetic of SVPWM, achieved the simulation result. And then this paper puts forward the inverter’s imagination based on neural network, when calculating the three-phase times, the paper uses a three-layer forward-feed network, adoptes the arithmetic of Levenberg-Marquarde to train the network. At last, in the environment of MATLAB/Simulink, the paper makes the simulation research to the system, the simulation results shows that the method of SVPWM based on neural network is feasible and it can achieve the anticipated effect, at the same time by using the neural network rationally, the method can enhance the PWM frequency and reduce the harmonic of output voltage.
本文首先对电力电子技术及逆变器的相关知识作了简要的介绍。对空间电压矢量脉宽调制的工作原理做了深入的研究,并且在此基础上构建了基于SVPWM算法的逆变器仿真模型,得到了仿真结果。之后本文提出了基于神经网络的逆变器的构想,在计算逆变器三相导通时间时,运用了3层前馈网络,并采用Levenberg-Marquardt算法对神经网络进行训练。最后,在MATLAB/Simulink环境下,对系统进行了仿真研究,仿真结果表明基于神经网络的SVPWM控制方法是可行的,能达到预期的效果,同时通过合理的运用神经网络,可以提高PWM的频率,减小输出电压中的谐波含量。
The inverter is used widely in society. With the rapid development of power electronics technology, a large amount of high-frequency switching equipment are appeared which provide the guarantee for the inverter’s application in all kinds of fields. The inverter’s main circuit discussed in this paper adopts the three-phase bridge structure. In terms of controller strategy, the traditional method of SPWM has the setback which is lower electric source utilization ratio. SVPWM is a kind of optimized method, it can reduce the output current’s harmonic component and improve the electric source utilization ratio, having the trend of replacing the traditional SPWM method, this paper just adopts the SVPWM as the controller strategy.
This paper firstly introduces the power electronics technology and inverter’s corresponding knowledge, and the paper deeply researches the work’s principle of the SVPWM and establishes the inverter’s simulation model based on the arithmetic of SVPWM, achieved the simulation result. And then this paper puts forward the inverter’s imagination based on neural network, when calculating the three-phase times, the paper uses a three-layer forward-feed network, adoptes the arithmetic of Levenberg-Marquarde to train the network. At last, in the environment of MATLAB/Simulink, the paper makes the simulation research to the system, the simulation results shows that the method of SVPWM based on neural network is feasible and it can achieve the anticipated effect, at the same time by using the neural network rationally, the method can enhance the PWM frequency and reduce the harmonic of output voltage.
引文
[1]王云亮等。电力电子变流技术[M].北京:电子工业出版社.2004.1-3。
[2]李永东等.脉宽调制(PWM)技术回顾、现状及展望.电气传动,1996,26 (3):2- 12。
[3]郝琇.逆变器技术的发展趋势.船电技术, 1997年04期
[4]刘慧博.基于神经网络的无刷直流电动机转子位置估计.微特电机[J],2003.3
[5] K.J.Huntetal.,“Neural networks for control systems-survey”[J],Automatic,vol.28.1083-1112,2000.
[6] Bimal K.Bose Modern Power Electronics and AC Drives机械工业出版社,2003
[7]陈坚.电力电子学[M].北京:高等教育出版社.2002.126-132.
[8]林渭勋.现代电力电子电路[M].杭州:浙江大学出版社,2002.
[9]张志涌.精通MATLAB6.5版.北京航空航大大学出版社,2003.
[10]程卫国,冯峰.姚东等.MATLABS.3应用指南.人民邮电出版社,1999.
[11]薛定宇,陈阳泉.基于MATLAB/SIMULINK的系统仿真技术与应用.清华大学出版社,2002:20-24.
[12]W James, Psychology[M], 1890, Holt Press, New York.
[13]W C McCulloch and W Pitts, A Logical Calculus of the Ideas Immanent in nervous activity[J], Bulletin of Mathematical Biophysics, 1943, No.5, 115-133.
[14]D O Hebb, The Organization of Behavior[J], 1949, John Wiley, New York.
[15]F Rosenblatt, The Perceptron: A Probabilistic Model for Information Storage and Organization in Brain[J], Psychological Review, 1958, No.65, 386-408.
[16]B Widrow and M E Hoff, Adaptive Switching Circuits[J], IRE WESCON Convention Record, P-art 4, Computers, Man-Machine Systems, 1960, 96-104.
[17]M Minsky and S Papert, Perceptrons[M], 1969, MIT Press, CA.
[18]J J Hopfield, Neural Network and Physical Systems with Emergent Collective Computational Abilities[J], Proc. Natl. Acad. Sci., 1982, No.79, 2554-2558.
[19]E Polak, Computational Methods in Optimization: A Unified Approach[M], 1971, Academic Press,New York.
[20]黄永安等.MATLAB7.0/Simulink6.0建模仿真开发与高级工程应用.北京:清华大学出版社,2005。
[21]王正林等.MATLAB/Simulink与控制系统仿真.北京:电子工业出版社,2005
[22]黄石生。逆变理论与弧焊逆变器[M].北京:机械工业出版社。1994。7-8。
[23]陈坚.电力电子学[M].北京:高等教育出版社.2002.319-327.
[24] Zhou Luowei,Luo Quanming.DC and AC analysis of three-phase three-switch boost type SMR based on DQ transformation.Proceedings of the CSEE.2002,22(7):71-75.
[25] Jung Y C,Liu H L,Cho G C et al.Soft switching space vector PWM inverter using a new quasi-parallel resonant dc link.IEEE Trans on Power Electronics,1996,11(5):503-511
[26] Malesani L, Tomasin P, Toino V.Space vector control and current harmonics in quasi resonant soft-switching PWM convver. IIJIJIJ Trans on Ind. Applicat, 1996, 32( 2):269一277
[27]王宝诚,张纯江等.谐振直流环对三相PWM整流器空间矢量PWM波形的影响.中国电机工程学报.2004年2月
[28]田亚菲等。电压空间矢量脉宽调制(SVPWM)算法仿真实现及分析.电力系统及其自动化学报,2004,16(4):68-71。
[29]王宝诚,张纯江等.谐振直流环对三相PWM整流器空间矢量PWM波形的影响.中国电机工程学报.2004年2月
[30]阮新波,严仰光.谐振直流环逆变器的参数设计及高频化.电力电子术.1995(2):9-11.
[31]庄圣贤,李学宁,李肇基.改进型并联准谐振直流环逆变器及脉冲调制.电子科技大学学报.1998年6月第三期.
[32]J.Holtz“Pulse width modulation for electric power conversion”, Proc.IEEE, vol.82, pp.1194-1214, Aug. 1994.
[33]J. O. P. Pinto, B. K. Bose, L. E. B. Silva, M. P. Karmierkowski“A Neural Network Based Space Vector PWM Controller for Voltage-Fed Inverter Induction Motor Drive”, IEEE Trans. on Ind. Appl., vol.36, no.6, November/December 2000.
[34]A.Bakhshai, J.Espinoza, G.Joos, H. Jin.“A combined ANN and DSP approach to the implementation of space vector modulation techniques”, in conf. Rec.IEEE–IAS Annu. Meeting, 1996, pp.934-940.
[35]F.Harashima et al.,“Applications of neural networks to power converter control”, in conf. Rec.IEEE–IAS Annu. Meeting, 1989,pp.1086-1091.
[36]M.R.Buhl and R.D.Lorenz,“Design and implementation of neural networks for digital current regulation of inverter drives”, in conf. Rec.IEEE–IAS Annu. Meeting, 1991, pp.415-423.
[37]J.W. Song, K.C. Lee, K. B. Cho, J. S. Won,“An adaptive learning current controller for field–oriented controlled induction motor by neural network”, in Proc. IEEE–IECON’91, 1991, pp.469-474.
[38]M. P. Karmierskowski et al.,“Neural network current control of VSPWM inverters”, in Proc. IPE’95, 1995, pp.1415-1420.
[39]N.V.Nho, M. J. Youn,“Two-mode overmodulation in two level VSI using principle control between limit trajectories”, CD-ROM Proc.PEDS 2003, pp.1274-1279
[40]J. Holtz, W. Lotzkat, M. Khambadkone,“On continuous control of PWM inverters in the overmodulation range includingthe six-step mode”, IEEE Trans. Power Electron., vol.8, pp.546-553, Oct. 1993.
[41]S. Bolognani, M. Ziglitti,“Novel digital continuous control of SVM inverters in the overmodulation range”, IEEE Trans. Ind. Applicat.,vol.33, pp.525-530, Mars/ April 1997.
[2]李永东等.脉宽调制(PWM)技术回顾、现状及展望.电气传动,1996,26 (3):2- 12。
[3]郝琇.逆变器技术的发展趋势.船电技术, 1997年04期
[4]刘慧博.基于神经网络的无刷直流电动机转子位置估计.微特电机[J],2003.3
[5] K.J.Huntetal.,“Neural networks for control systems-survey”[J],Automatic,vol.28.1083-1112,2000.
[6] Bimal K.Bose Modern Power Electronics and AC Drives机械工业出版社,2003
[7]陈坚.电力电子学[M].北京:高等教育出版社.2002.126-132.
[8]林渭勋.现代电力电子电路[M].杭州:浙江大学出版社,2002.
[9]张志涌.精通MATLAB6.5版.北京航空航大大学出版社,2003.
[10]程卫国,冯峰.姚东等.MATLABS.3应用指南.人民邮电出版社,1999.
[11]薛定宇,陈阳泉.基于MATLAB/SIMULINK的系统仿真技术与应用.清华大学出版社,2002:20-24.
[12]W James, Psychology[M], 1890, Holt Press, New York.
[13]W C McCulloch and W Pitts, A Logical Calculus of the Ideas Immanent in nervous activity[J], Bulletin of Mathematical Biophysics, 1943, No.5, 115-133.
[14]D O Hebb, The Organization of Behavior[J], 1949, John Wiley, New York.
[15]F Rosenblatt, The Perceptron: A Probabilistic Model for Information Storage and Organization in Brain[J], Psychological Review, 1958, No.65, 386-408.
[16]B Widrow and M E Hoff, Adaptive Switching Circuits[J], IRE WESCON Convention Record, P-art 4, Computers, Man-Machine Systems, 1960, 96-104.
[17]M Minsky and S Papert, Perceptrons[M], 1969, MIT Press, CA.
[18]J J Hopfield, Neural Network and Physical Systems with Emergent Collective Computational Abilities[J], Proc. Natl. Acad. Sci., 1982, No.79, 2554-2558.
[19]E Polak, Computational Methods in Optimization: A Unified Approach[M], 1971, Academic Press,New York.
[20]黄永安等.MATLAB7.0/Simulink6.0建模仿真开发与高级工程应用.北京:清华大学出版社,2005。
[21]王正林等.MATLAB/Simulink与控制系统仿真.北京:电子工业出版社,2005
[22]黄石生。逆变理论与弧焊逆变器[M].北京:机械工业出版社。1994。7-8。
[23]陈坚.电力电子学[M].北京:高等教育出版社.2002.319-327.
[24] Zhou Luowei,Luo Quanming.DC and AC analysis of three-phase three-switch boost type SMR based on DQ transformation.Proceedings of the CSEE.2002,22(7):71-75.
[25] Jung Y C,Liu H L,Cho G C et al.Soft switching space vector PWM inverter using a new quasi-parallel resonant dc link.IEEE Trans on Power Electronics,1996,11(5):503-511
[26] Malesani L, Tomasin P, Toino V.Space vector control and current harmonics in quasi resonant soft-switching PWM convver. IIJIJIJ Trans on Ind. Applicat, 1996, 32( 2):269一277
[27]王宝诚,张纯江等.谐振直流环对三相PWM整流器空间矢量PWM波形的影响.中国电机工程学报.2004年2月
[28]田亚菲等。电压空间矢量脉宽调制(SVPWM)算法仿真实现及分析.电力系统及其自动化学报,2004,16(4):68-71。
[29]王宝诚,张纯江等.谐振直流环对三相PWM整流器空间矢量PWM波形的影响.中国电机工程学报.2004年2月
[30]阮新波,严仰光.谐振直流环逆变器的参数设计及高频化.电力电子术.1995(2):9-11.
[31]庄圣贤,李学宁,李肇基.改进型并联准谐振直流环逆变器及脉冲调制.电子科技大学学报.1998年6月第三期.
[32]J.Holtz“Pulse width modulation for electric power conversion”, Proc.IEEE, vol.82, pp.1194-1214, Aug. 1994.
[33]J. O. P. Pinto, B. K. Bose, L. E. B. Silva, M. P. Karmierkowski“A Neural Network Based Space Vector PWM Controller for Voltage-Fed Inverter Induction Motor Drive”, IEEE Trans. on Ind. Appl., vol.36, no.6, November/December 2000.
[34]A.Bakhshai, J.Espinoza, G.Joos, H. Jin.“A combined ANN and DSP approach to the implementation of space vector modulation techniques”, in conf. Rec.IEEE–IAS Annu. Meeting, 1996, pp.934-940.
[35]F.Harashima et al.,“Applications of neural networks to power converter control”, in conf. Rec.IEEE–IAS Annu. Meeting, 1989,pp.1086-1091.
[36]M.R.Buhl and R.D.Lorenz,“Design and implementation of neural networks for digital current regulation of inverter drives”, in conf. Rec.IEEE–IAS Annu. Meeting, 1991, pp.415-423.
[37]J.W. Song, K.C. Lee, K. B. Cho, J. S. Won,“An adaptive learning current controller for field–oriented controlled induction motor by neural network”, in Proc. IEEE–IECON’91, 1991, pp.469-474.
[38]M. P. Karmierskowski et al.,“Neural network current control of VSPWM inverters”, in Proc. IPE’95, 1995, pp.1415-1420.
[39]N.V.Nho, M. J. Youn,“Two-mode overmodulation in two level VSI using principle control between limit trajectories”, CD-ROM Proc.PEDS 2003, pp.1274-1279
[40]J. Holtz, W. Lotzkat, M. Khambadkone,“On continuous control of PWM inverters in the overmodulation range includingthe six-step mode”, IEEE Trans. Power Electron., vol.8, pp.546-553, Oct. 1993.
[41]S. Bolognani, M. Ziglitti,“Novel digital continuous control of SVM inverters in the overmodulation range”, IEEE Trans. Ind. Applicat.,vol.33, pp.525-530, Mars/ April 1997.
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