基于智能控制的双馈风力发电机的功率解耦控制
摘要
随着科学技术的迅速发展,能源消耗不断增长、环境污染也越来越严重,人们不得不考虑新能源和可再生能源的开发和利用问题。而在众多的可再生能源中,风能以其突出的优点而倍受世界的关注。在风力发电系统中,变速恒频双馈风力发电技术由于其低成本、高效率等特点成为当今风力发电技术中的主流。本文针对双馈风力发电机组的功率解耦控制问题进行了研究。
首先介绍了国内外风电的发展状况、功率解耦控制的研究现状;从双馈风力发电机组的基本原理入手,给出风速、风力机和在不同坐标系下双馈风力发电机的数学模型,对双馈风力发电机的稳态运行进行了分析。其次,研究了基于定子磁链定向矢量控制的功率解耦控制系统,得到解耦控制系统的数学模型,在此基础上,建立了双馈风力发电机的双闭环控制模型。再次,针对双馈风力发电机矢量控制中功率外环的传统PI调节器的参数自动调节能力差,跟随性能差等缺点,本文将神经网络与PI调节器相结合,设计出BP神经网络PI控制器,对BP神经网络PI控制器进行性能仿真,仿真结果表明BP神经网络PI控制器具有较好的动态性能。
在MATLAB软件中建立基于BP神经网络PI控制器的双馈风力发电系统的功率解耦控制系统的仿真模型,并且针对该控制系统,在不同的风速下进行系统仿真,仿真结果表明,该控制系统不仅实现了双馈风力发电系统有功功率和无功功率的完全解耦,而且在风速变化时,双馈风力发电机组的输出功率跟踪效果较好,系统性能稳定,证明了控制系统具有良好的动态性能。
With the rapid technological development, energy consumption is growing and the environmental pollution is more and more serious, so that people have to consider new energy and renewable energy development and utilization. In a number of renewable energy sources, with its outstanding advantages wind energy get much world's attention. In wind power generation system, VSCF doubly-fed wind power generation technology has become today's mainstream because of its low cost, high efficiency characteristics. In this paper, decoupling control of double-fed wind power generator is studied.
Firstly, the development of wind power at home and abroad and power decoupling control Research are introduced; starting from the basic principles, the mathematical models of wind speed, wind turbine and the double-fed wind power generator in different coordinate system are bulit, and steady-state operation of it is analyzed. Secondly, the power decoupling control based on the stator flux oriented vector control is studied, and a mathematical model of decoupled control system is given; on this basis, the double-loop control model is built. Again, aiming to the traditional PI regulators of power outer loop being poor self-regulation capacity and following poor performance shortcomings, a BP neural network PI controller which is the combination of neural network and the PI regulator is designed. For the BP neural network PI controller performance simulation, simulation results show that the BP neural network PI controller has better dynamic performance.
Power decoupling control system based on BP neural network PI controller is created in the MATLAB software. System simulation is taken at different wind speeds, and simulation results show that the control system not only achieves the completely decoupled of power for double-fed wind power generation system, but also the control system output power has better tracking performance, system performance and stability under the changes in wind speed,and it has good dynamic performance.
首先介绍了国内外风电的发展状况、功率解耦控制的研究现状;从双馈风力发电机组的基本原理入手,给出风速、风力机和在不同坐标系下双馈风力发电机的数学模型,对双馈风力发电机的稳态运行进行了分析。其次,研究了基于定子磁链定向矢量控制的功率解耦控制系统,得到解耦控制系统的数学模型,在此基础上,建立了双馈风力发电机的双闭环控制模型。再次,针对双馈风力发电机矢量控制中功率外环的传统PI调节器的参数自动调节能力差,跟随性能差等缺点,本文将神经网络与PI调节器相结合,设计出BP神经网络PI控制器,对BP神经网络PI控制器进行性能仿真,仿真结果表明BP神经网络PI控制器具有较好的动态性能。
在MATLAB软件中建立基于BP神经网络PI控制器的双馈风力发电系统的功率解耦控制系统的仿真模型,并且针对该控制系统,在不同的风速下进行系统仿真,仿真结果表明,该控制系统不仅实现了双馈风力发电系统有功功率和无功功率的完全解耦,而且在风速变化时,双馈风力发电机组的输出功率跟踪效果较好,系统性能稳定,证明了控制系统具有良好的动态性能。
With the rapid technological development, energy consumption is growing and the environmental pollution is more and more serious, so that people have to consider new energy and renewable energy development and utilization. In a number of renewable energy sources, with its outstanding advantages wind energy get much world's attention. In wind power generation system, VSCF doubly-fed wind power generation technology has become today's mainstream because of its low cost, high efficiency characteristics. In this paper, decoupling control of double-fed wind power generator is studied.
Firstly, the development of wind power at home and abroad and power decoupling control Research are introduced; starting from the basic principles, the mathematical models of wind speed, wind turbine and the double-fed wind power generator in different coordinate system are bulit, and steady-state operation of it is analyzed. Secondly, the power decoupling control based on the stator flux oriented vector control is studied, and a mathematical model of decoupled control system is given; on this basis, the double-loop control model is built. Again, aiming to the traditional PI regulators of power outer loop being poor self-regulation capacity and following poor performance shortcomings, a BP neural network PI controller which is the combination of neural network and the PI regulator is designed. For the BP neural network PI controller performance simulation, simulation results show that the BP neural network PI controller has better dynamic performance.
Power decoupling control system based on BP neural network PI controller is created in the MATLAB software. System simulation is taken at different wind speeds, and simulation results show that the control system not only achieves the completely decoupled of power for double-fed wind power generation system, but also the control system output power has better tracking performance, system performance and stability under the changes in wind speed,and it has good dynamic performance.
引文
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[5]王伟,陈宁,朱凌志等.双馈风力发电机低电压过渡的相角补偿控制策略.中国电机工程学报,2009,29(21):62~68.
[6] Kayik?i M, Milanovic J V. Dynamic contribution of DFIG-based wind plants to system frequency disturbances. IEEE Transaction on Power Systems, 2009, 24(2):859~867.
[7] Konopinski R J, Vijayan P, Ajjarapu V. Extended reactive capability of DFIG wind parks for enhanced system performance. IEEE Transaction on Power Systems, 2009, 24(3):1346~1355.
[8] Tapia G, Giovanna S, Mikel T et al. Methodology for smooth connection of doubly fed induction generators to the grid. IEEE Transaction on Energy Conversion, 2009, 24(4):959~971.
[9]刘其辉,贺益康,卞松江.变速恒频风力发电机空载并网控制研究.中国电机工程学报,2004,24(3):6~11.
[10]北京清能华福风电技术有限公司研发部.变频技术在变速恒频异步风力发电系统中的应用.变频器世界,2008(1):27~29.
[11]吴国祥,黄建明,陈国呈等.变速恒频双馈风力发电运行综合控制策略.电机与控制学报, 2008(4):45~48.
[12]代毅.基于P-Q解耦控制的并网双馈风力发电系统小信号稳定性分析:(硕士学位论文).合肥:合肥工业大学,2009.
[13]赵新一.新能源发展展望.电力技术,2009(10):7~14.
[14]周艳莉.风力发电的现状与发展趋势.甘肃科技,2008,24(3):9~11.
[15]蔡凯,谭伦农,李春林等.时间序列与神经网络法相结合的短期风速预测.电网技术,2008,32(8):82~90.
[16] Tomonobu HS,H HRyosei S.H Output power leveling of wind farm using pitch angle control with fuzzy neural network. Electric Power Components and Systems, 2008, 36(10): 1048-1066.
[17] Barambones O. A robust wind turbine control using a Neural Network based wind speed estimator. 2010 International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, 2010:1~8.
[18] Barambones O, de Durana, J.M.G.,Kremers, E. A Neural Network Based Wind Speed Estimator for a Wind Turbine Control. MELECON 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference, Valletta, Malta, 2010:1383~1388.
[19] Bati A F, Leabi S K. NN Self-Tuning Pitch Angle Controller of Wind Power Generation Unit. HPower Systems Conference and Exposition, Atlanta, GA, USA, 2006H:2019~2029.
[20]潘俊强,王维庆,张尧等.神经网络模型参考控制在风力发电机组变桨距系统的应用.机械制造与自动化,2009,38(5):144~146.
[21]师彪,李郁侠,于新花等.基于弹性自适应人工鱼群-BP神经网络的风轮节距控制环.农业工程学报,2010,26(1):145~149.
[22] Ren Y F, Bao G Q. Control Strategy of Maximum Wind Energy Capture of Direct-Drive Wind Turbine Generator Based on Neural-Network. Proceedings of the 2010 Asia-Pacific Power and Energy Engineering Conference (APPEEC 2010), Chengdu, China ,2010:1~4.
[23]周培毅,张新燕,张华中.基于遗传算法与BP神经网的风力发电机齿轮箱故障诊断研究.华北电力技术,2010(7):6~11.
[24]赵卓鹏,贾石峰.基于Elman神经网络的风力发电机组齿轮箱故障诊断研究.伺服控制,2010(6):65~66.
[25] Shi K L, Li H. A novel control of a small wind turbine driven generator based on neural networks. Piscataway N J. 2004 IEEE Power Engineering Society General Meeting, Denver, CO, USA , 2004:166~169.
[26] Ko H S, Niimura T, Lee K Y. An intelligent controller for a remote wind-diesel power system - design and dynamic performance analysis. HPower Engineering Society General MeetingH, 2003, 4:2147~2152.
[27]许凌峰,徐大平,高峰等.基于神经网络的风力发电机组变桨距复合控制.华北电力大学学报,2009,36(1):28~34.
[28] Sedighizadeh M, Rezazadeh A. Adaptive PID Control of Wind Energy Conversion Systems Using RASP1 Mother Wavelet Basis Function Networks. IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering, Chiang Mai, Thailand ,2004: 524~527.
[29] Li H, Zhang D, Simon Y. A Stochastic Digital Implementation of a Neural Network Controller for Small Wind Turbine Systems. HPower ElectronicsH, 2006, 21(5):1502~1507.
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