大型风力发电机偏航系统控制策究现状及展望

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Abstract Yaw system control strategy directly determines the economic benefits of wind power system. According to the operational principle, yaw system control strategy is divided into reboot tracing wind strategy and implement tracing wind strategy, and research status is reviewed separately. Analysis results show that current research hotspot is implement tracing wind strategy of yaw system, but new research on the reboot tracing wind strategy of the yaw system is less. Based on quantitative analysis results of yaw system real operation data, field wind source data and power curve of wind turbine, this paper points out that the traditional reboot tracing strategy of yaw system has the defect of the more lifetime of yaw system consumed in low wind speed segment, and influences comprehensive benefit of yaw system life cycle; formulating yaw system reboot wind tracing strategy should comprehensive consider wind farm wind source characteristics and wind turbine power generation characteristic, and there is great value that carried out new reboot wind tracing strategy to realize the efficient utilization of the yaw system operation life and should be concerned.

Key words： Wind turbines yaw system control strategy economic operation

Received: 10 September 2014 Published: 29 June 2015

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TM315

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	Shen Xiaojun
	Du Wanli

Cite this article:

Shen Xiaojun,Du Wanli. Expectation and Review of Control Strategy of Large Wind Turbines Yaw System[J]. Transactions of China Electrotechnical Society, 2015, 30(10): 196-203.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I10/196

[1] Ling S, Yongjun G, Zuwen W, et al. Design of large- scale wind power yawing bearing test-bed control system[C]. IEEE International Conference on Fluid Power and Mechatronics (FPM), 2011: 319-323.
[2] Denny E, O'Malley M. Wind generation, power system operation, and emissions reduction [J]. IEEE Transac- tions on Power Systems, 2006, 21(1): 341-347.
[3] 叶杭冶, 风力发电机组的控制技术[M]. 北京: 机械工业出版社, 2002.
[4] Kojabadi H M, Chang L, Boutot T. Development of a novel wind turbine simulator for wind energy conversion systems using an inverter-controlled induction motor [J]. IEEE Transactions on Energy Conversion, 2004, 19(3): 547-552.
[5] Zhang Y, Ula S. Comparison and evaluation of three main types of wind turbines[C]. IEEE Transmission and Distribution Conference and Exposition, 2008: 1-6.
[6] Jacobson S H, Yücesan E. Analyzing the performance of generalized hill climbing algorithms [J]. Journal of Heuristics, 2004, 10(4): 387-405.
[7] 李晓燕, 王志新. 风力机偏航控制策略及系统设计[J]. 微计算机信息, 2007, 23(25): 1-3. Li Xiaoyan, Wang Zhixin. Yaw Cnotrol strategy and system design for wind turbine[J]. Micro Computer Information, 2007, 23(25): 1-3.
[8] 王志新, 张华强. 风力发电技术与功率控制策略研究[J]. 自动化仪表, 2008, 29(11): 2-4. Wang Zhixin, Zhang Huaqiang. Research on wind enegy generation technology and power control strategy [J]. Automation Instrumentation, 2008, 29(11): 2-4.
[9] 顾露香, 乐秀璠, 杨虞琨, 等. KHC算法在风力发电机组偏航系统的运用[J]. 华电技术, 2011, 33(9): 92-94. Gu Luxiang, Le Xiufan, Yang Yukun, et al. Applica- tion of KHC algorithm in wind turbine yaw system[J]. Huadian Technology, 2011, 33(9): 92-94.
[10] Piao H, Wang Z, Zhang H. Cooperative-PSO-based new learning algorithm for PID neural network and nonlinear control design[J]. The Mediterranean Journal of Measurement and Control, 2009, 5(2): 60-70.
[11] Zhao L, Yang Y. PSO-based single multiplicative neuron model for time series prediction [J]. Expert Systems with Applications, 2009, 36(2): 2805-2812.
[12] 朴海国, 王志新. 基于CPSO的PID神经网络及偏航电机控制策略[J]. 电机与控制学报, 2010, 14(9): 55-62. Piao Haiguo, Wang Zhixin. Control strategy of CPSO- based PID neural network and a yaw motor[J]. Electric Machines and Control, 2010, 14(9): 55-62.
[13] Narendra K S, Mukhopadhyay S. Adaptive control using neural networks and approximate models[J]. IEEE Transactions on Neural Networks, 1997, 8(3): 475-485.
[14] 李毅, 温正忠, 赵少刚, 等. 风力发电机偏航系统模糊控制的研究[J]. 现代机械, 2007(1): 29-30. Li Yi, Wen Zhengzhong, Zhao Shaogang, et al. Resarch on Fuzzy logic controlling of wind driven-generator yaw system[J]. Modern Machinery, 2007(1): 29-30.
[15] 李毅, 温正忠. 风力发电偏航系统的PID-Fuzzy分段复合控制研究[J]. 机械设计与制造, 2007(4): 54-55. Li Yi, Wen Zhengzhong. Study on PID-Fuzzy composite control for yaw system of wind driven-generator[J]. Machines Design and Manufacture, 2007(4): 54-55.
[16] Chen F, Yang J. Fuzzy PID controller used in yaw system of wind turbine[C]. IEEE 3rd International Conference on Power Electronics Systems and Applica- tions, 2009: 1-4.
[17] 朴海国, 王志新. 风电机组偏航Fuzzy-PID合成控制系统仿真[J]. 电工技术学报, 2009, 24(3): 183-188. Piao Haiguo, Wang Zhixin. Simulation of fuzzy-PID synthesis yawing control system of wind turbine[J]. Transactions of China Electrotechnical Socity, 2009, 24(3): 183-188.
[18] Chenghui Z, Pengju L, Jianping W, et al. Research on intelligent controller of wind-power yaw based on modulation of artificial neuro-endocrine-immunity system[J]. Procedia Engineering, 2011(15): 903-907.
[19] Arabian Hoseynabadi H, Oraee H, Tavner P J. Failure modes and effects analysis(FMEA) for wind turbines [J]. International Journal of Electrical Power & Energy Systems, 2010, 32(7): 817-824.
[20] 王秋芬, 黄芳林. 基于Ansys的大型风电机组偏航连接系统计算方法研究[J]. 机械强度, 2011, 33(4): 607-612. Wang Qiufen, Huang Fanglin. Study on the calculation method of yawing connect system of large wind turbines based on Ansys[J]. Journal of Mechinal Strength, 2011, 33(4): 607-612.