变速变桨距风力发电机组的限功率控制通常采用变桨距控制技术。该方法在高风速时能通过调节桨距角来快速稳定的控制功率输出和风机转速,但在中低风速时,却没有充分利用风力机特性,以优化风机运行工况。该文在综合分析全风速限功率控制...变速变桨距风力发电机组的限功率控制通常采用变桨距控制技术。该方法在高风速时能通过调节桨距角来快速稳定的控制功率输出和风机转速,但在中低风速时,却没有充分利用风力机特性,以优化风机运行工况。该文在综合分析全风速限功率控制特性基础上,提出一种主动变速和桨距角控制相结合的新型限功率控制策略(novel wind power curtailment control,N-WPCC)。理论分析和仿真结果表明,与传统限功率控制相比,N-WPCC优先进行电磁转矩控制,再进行桨距角控制,能有效减少变桨系统的动作频率和动作幅度,提高变桨系统的使用寿命,并能充分利用机组转动惯量,在一定程度上提高发电量。同时,N-WPCC的控制输入为机组输出功率和电机转速,不需要可靠性不高的现场实时测风数据。展开更多
According to the theory of Matsuoka neural oscillators and with the con- sideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator （CPG） neural network c...According to the theory of Matsuoka neural oscillators and with the con- sideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator （CPG） neural network consisting of six neurons is pro- posed to regulate the contraction of the upper arm muscles. To verify effectiveness of the proposed CPG network, an arm motion control model based on the CPG is established. By adjusting the CPG parameters, we obtain the neural responses of the network, the angles of joint and hand of the model with MATLAB. The simulation results agree with the results of crank rotation experiments designed by Ohta et al., showing that the arm motion control model based on a CPG network is reasonable and effective.展开更多
文摘变速变桨距风力发电机组的限功率控制通常采用变桨距控制技术。该方法在高风速时能通过调节桨距角来快速稳定的控制功率输出和风机转速,但在中低风速时,却没有充分利用风力机特性,以优化风机运行工况。该文在综合分析全风速限功率控制特性基础上,提出一种主动变速和桨距角控制相结合的新型限功率控制策略(novel wind power curtailment control,N-WPCC)。理论分析和仿真结果表明,与传统限功率控制相比,N-WPCC优先进行电磁转矩控制,再进行桨距角控制,能有效减少变桨系统的动作频率和动作幅度,提高变桨系统的使用寿命,并能充分利用机组转动惯量,在一定程度上提高发电量。同时,N-WPCC的控制输入为机组输出功率和电机转速,不需要可靠性不高的现场实时测风数据。
基金supported by the National Natural Science Foundation of China(Nos.11232005 and11472104)
文摘According to the theory of Matsuoka neural oscillators and with the con- sideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator （CPG） neural network consisting of six neurons is pro- posed to regulate the contraction of the upper arm muscles. To verify effectiveness of the proposed CPG network, an arm motion control model based on the CPG is established. By adjusting the CPG parameters, we obtain the neural responses of the network, the angles of joint and hand of the model with MATLAB. The simulation results agree with the results of crank rotation experiments designed by Ohta et al., showing that the arm motion control model based on a CPG network is reasonable and effective.
