采样迭代学习发电机的励磁控制器设计

Excitation controller design for generator based on sampled iterative learning

为了提高电力系统的暂态稳定性,采用采样迭代学习方法,在不需要已知被控系统精确数学模型和对机端电压误差进行求导运算的情况下,设计出了一种同步发电机的励磁控制器,给出了励磁控制系统范数形式的收敛条件.研究结果表明:该控制器避免了以往采用PD型迭代学习控制算法的不足,显著提高了同步发电机功角和机端电压的稳定性.该成果不仅解决了在实际应用中迭代学习励磁控制算法的计算机实现问题,而且还将可能成为发电机的励磁控制方式发展的一种趋势.

in order to enhance the transient stability of the power system as well as avoid the requirement for the exact mathematical model of the controlled system and differential operation on error of the terminal voltage, this paper adopted sampled-data iterative learning algorithm, designed an excitation controller of synchronous generator, and provided the convergent condition of norm form of excitation control system. The results of the study suggest that the controller avoids the shortcomings of past PD-type iterative learning control and significantly improve the stabilities of synchronous generator power-angle and terminal voltage. This achievement not only solves the problem of computer realization of iterative learning excitation control in practice, but also maybe become a kind of development trend of the mode of generator excitation control.