针对海上直驱永磁风力发电机在风速大于额定风速下输出功率不稳定、影响电力质量的问题,提出一种RBF神经网络实时在线调整PID参数的变桨距控制策略,利用神经网络的自学习能力让内部参数不断优化,使系统更适应非线性和时变性。为进一步提高桨距角变化响应速度和输出功率的稳定性,设计一种敏感变量指数趋近律滑模控制器嵌入到发电机机侧速度环中,敏感变量可提高系统的收敛速度和抗抖振能力。在Matlab/Simulink上搭建直驱式永磁风力发电机的各部分仿真模块并进行对比实验,结果表明,基于RBF-PID和敏感变量滑模趋近律的变桨控制策略相比于改进前,桨距角变化响应更迅速,输出功率更稳定且震荡率只有0.4%,达到预期控制效果。
Abstract
A variable pitch control strategy based on RBF neural network for real-time online adjustment of PID parameters is proposed to address the issue of unstable output power and impact on power quality of offshore direct drive permanent magnet wind turbines when wind speed exceeds rated wind speed. The self-learning ability of the neural network is utilized to continuously optimize internal parameters, making the system more adaptable to nonlinearity and time-varying. In order to further improve the response speed of pitch angle change and the stability of output power, a sensitive variable exponential reaching law sliding mode controller is designed and embedded into the generator side speed loop. The sensitive variable can improve the Rate of convergence and anti chattering ability of the system. Build simulation modules for various parts of the direct drive permanent magnet wind turbine generator on Matlab/Simulink and conduct comparative experiments. The results show that the pitch control strategy based on RBF-PID and sensitive variable sliding mode approach law has a faster response to pitch angle changes, more stable output power, and a oscillation rate of only 0.4% compared to before improvement, achieving the expected control effect.
关键词
风力发电机 /
电力质量 /
神经网络 /
敏感变量 /
滑模控制
Key words
wind turbines /
power quality /
neural network /
sensitive variables /
sliding mode control
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基金
国家A类重大攻关项目（E210E001010）; 江西省教育厅科技项目（GJJ2200809）
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