摘要
针对风电系统处于欠功率阶段时,风能利用系数必须保持在最大值处的问题,提出了一种基于小波神经网络PID控制器的最大功率点跟踪控制策略。该策略采用小波神经网络对风能利用系数进行在线辨识,1个辨识周期结束后返回此时的灵敏度信息,PID控制器根据该灵敏度信息调整PID参数。仿真结果表明,与常规PID控制器相比,小波神经网络PID控制器提高了风电系统的叶尖速比、风能利用系数和输出功率,缩短了响应时间,实现了风力发电机组的优化运行。
In view of problem that power coefficient must keep maximum value when wind power system is in underpower stage, the paper proposed a control strategy of maximum power point tracking based on wavelet neural network and PID controller. The strategy uses wavelet neural network to make online identification for power coefficient and returns sensitivity information after ending of one identification period. PID controller adjusts PID parameters according to the sensitivity information. Simulation result shows that wavelet neural network and PID controller improves blade top speed ratio, power coefficient and output power of wind power system and reduces response time compared with common PID controller, which realizes optimal running of wind turbines.
出处
《工矿自动化》
北大核心
2013年第12期73-76,共4页
Journal Of Mine Automation
