Impact of signal transmission delays on power system damping control using heuristic dynamic programming
Impact of signal transmission delays on power system damping control using heuristic dynamic programming
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DOI：
10.1109/ciasg.2014.7011567
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Yufei Tang;Xiangnan Zhong;Zhen Ni;Jun Yan;Haibo He
中科院分区：
文献类型：
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作者：
Yufei Tang;Xiangnan Zhong;Zhen Ni;Jun Yan;Haibo He
本文研究了信号传输延迟对基于静态无功补偿器（SVC）的电力系统阻尼控制的影响。SVC用于在故障条件下抑制互联电力系统之间的低频振荡，其中首先收集来自偏远地区的测量信号，然后将其作为输入传输到控制器。在这种设计中引入了不可避免的信号传输延迟，这会降低SVC的动态性能，在最坏的情况下会导致系统不稳定。采用目标表示启发式动态规划（GrHDP）强化学习算法设计SVC控制器。在Matlab/Simulink环境下，通过基于全暂态模型的时域仿真，研究了信号传输时延对所采用控制器的影响。基于SVC的四机两区基准系统的仿真结果表明，所采用的算法对阻尼控制和信号传输延迟的影响是有效的。
In this paper, the impact of signal transmission delays on static VAR compensator (SVC) based power system damping control using reinforcement learning is investigated. The SVC is used to damp low-frequency oscillation between interconnected power systems under fault conditions, where measured signals from remote areas are first collected and then transmitted to the controller as the inputs. Inevitable signal transmission delays are introduced into such design that will degrade the dynamic performance of SVC and in the worst case, cause system instability. The adopted reinforcement learning algorithm, called goal representation heuristic dynamic programming (GrHDP), is employed to design the SVC controller. Impact of signal transmission delays on the adopted controller is investigated with fully transient model based time-domain simulation in Matlab/Simulink environment. The simulation results on a four-machine two-area benchmark system with SVC demonstrate the effectiveness of the adopted algorithm on damping control and the impact of signal transmission delays.