| 引用本文: | 曹松青,郝万君,郝诗源,等.基于超扭曲优化算法的风机最大功率跟踪控制[J].电力系统保护与控制,2019,47(15):61-68.[点击复制] |
| CAO Songqing,HAO Wanjun,HAO Shiyuan,et al.Maximum power tracking control of wind turbine based on super twisting optimization algorithm[J].Power System Protection and Control,2019,47(15):61-68[点击复制] |
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| 摘要: |
| 针对风力发电系统在低风速区采用传统控制方法具有风能转换效率较低、风轮转速跟踪实时风速的性能较差、发电机转矩波动范围较大等问题，提出了一种将超扭曲算法与最佳转矩法相结合的最大功率跟踪改进控制策略。为进一步改善控制性能，采用粒子群算法对控制器参数进行优化。最后以风轮角速度、发电机输出功率、发电机转矩、功率系数等为评价指标，通过Matlab/Simulink平台验证所提方法的可行性与有效性。仿真结果表明，所提控制策略在提高最大风能捕捉能力的同时可有效地抑制发电机转矩的抖振。 |
| 关键词: 超扭曲算法 风力发电机组 最大功率跟踪 粒子群算法 抖振 |
| DOI：10.7667/PSPC20191509 |
| 投稿时间：2018-11-25修订日期：2019-01-18 |
| 基金项目:国家自然科学基金项目(51477109，61703296) |
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| Maximum power tracking control of wind turbine based on super twisting optimization algorithm |
| CAO Songqing,HAO Wanjun,HAO Shiyuan,CHEN Xinjing,WANG Hao,SUN Zhihui |
| (Institute of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China;Department of Electrical Engineering, Technical University of Denmark, Lyngby 999017, Denmark) |
| Abstract: |
| When traditional control methods are used in low-speed region of wind power generation power systems, there are several problems including low efficiency of wind energy conversion, poor performance of tracking wind speed, and large fluctuation range of generation torque, etc. Against these problems, an improved control strategy, which combines super twisting algorithm with optimal torque method for maximum power point tracking, is proposed. In order to further improve the control performance, the particle swarm algorithm is used to optimize the controller parameters. Finally, the wind rotor angular velocity, generator output power, generator torque and power coefficient are used as evaluation indexes, and the feasibility and effectiveness of the proposed method are verified on Matlab/Simulink platform. The simulation results show that the proposed control strategy can effectively suppress the chattering of generator torque while improving the capability of capturing the maximum wind energy. This work is supported by National Natural Science Foundation of China (No. 51477109 and No. 61703296). |
| Key words: super twisting algorithm wind turbine maximum power point tracking particle swarm algorithm chattering |
