IEEE Trans Neural Netw Learn Syst. 2016 Aug;27(8):1762-72. doi: 10.1109/TNNLS.2015.2504035. Epub 2015 Dec 17.
This paper develops an adaptive modulation approach for power system control based on the approximate/adaptive dynamic programming method, namely, the goal representation heuristic dynamic programming (GrHDP). In particular, we focus on the fault recovery problem of a doubly fed induction generator (DFIG)-based wind farm and a static synchronous compensator (STATCOM) with high-voltage direct current (HVDC) transmission. In this design, the online GrHDP-based controller provides three adaptive supplementary control signals to the DFIG controller, STATCOM controller, and HVDC rectifier controller, respectively. The mechanism is to observe the system states and their derivatives and then provides supplementary control to the plant according to the utility function. With the GrHDP design, the controller can adaptively develop an internal goal representation signal according to the observed power system states, therefore, to achieve more effective learning and modulating. Our control approach is validated on a wind power integrated benchmark system with two areas connected by HVDC transmission lines. Compared with the classical direct HDP and proportional integral control, our GrHDP approach demonstrates the improved transient stability under system faults. Moreover, experiments under different system operating conditions with signal transmission delays are also carried out to further verify the effectiveness and robustness of the proposed approach.
本文提出了一种基于近似/自适应动态规划方法（即目标表示启发式动态规划（GrHDP））的电力系统控制自适应调制方法。具体而言，我们关注双馈感应发电机（DFIG）风力发电场和高压直流（HVDC）输电的静止同步补偿器（STATCOM）的故障恢复问题。在该设计中，基于在线 GrHDP 的控制器分别为 DFIG 控制器、STATCOM 控制器和 HVDC 整流器控制器提供三个自适应补充控制信号。其机制是观察系统状态及其导数，然后根据效用函数为工厂提供补充控制。通过 GrHDP 设计，控制器可以根据观察到的电力系统状态自适应地开发内部目标表示信号，从而实现更有效的学习和调节。我们的控制方法在通过 HVDC 传输线连接的两个区域的风力发电综合基准系统上进行了验证。与经典的直接 HDP 和比例积分控制相比，我们的 GrHDP 方法在系统故障下表现出更好的暂态稳定性。此外，还进行了不同系统运行条件下带有信号传输延迟的实验，以进一步验证所提出方法的有效性和鲁棒性。
