并网同步器的双重启发式动态规划控制
Dual Heuristic Dynamic Programing Control of Grid-Connected Synchronverters
摘要：
本文提出了一种利用双启发式动态规划（DHP）设计控制并网同步发电机的新方法。针对传统的同步变流控制器在处理非线性、不确定性和无感电网时所面临的挑战，提出了一种基于神经网络的自适应评价设计方法来优化相关的代价函数。神经网络的特性有助于在不确定性和未知参数（例如不同的功率角）下的性能。建议的DHP设计包括三个神经网络：系统NN，行动NN，和批评NN。仿真结果比较了所提出的DHP与传统的基于PI的设计和神经网络预测控制器的性能。它示出了一个良好的训练DHP设计执行的轨迹，这是更优的相比，其他两个控制器。
英文摘要：
In this paper a new approach to control a grid-connected synchronverter by using a dual heuristic dynamic programing (DHP) design is presented. The disadvantages of conventional synchronverter controller such as the challenges to cope with nonlinearity, uncertainties, and non-inductive grids are discussed.To deal with the aforementioned challenges a neural network based adaptive critic design is introduced to optimize the associated cost function. The characteristic of the neural networks facilitates the performance under uncertainties and unknown parameters (for example different power angles). The proposed DHP design includes three neural networks: system NN, action NN, and critic NN. The simulation results compare the performance of the proposed DHP with a traditional PI-based design and with a neural network predictive controller. It is shown a well trained DHP design performs in a trajectory, which is more optimal compared to the other two controllers.
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