论文标题
基于机器学习的微电网稳定的最佳反馈控制
Machine Learning based Optimal Feedback Control for Microgrid Stabilization
论文作者
论文摘要
与常规电网相比，微电网具有更多的操作灵活性和不确定性，尤其是在利用可再生能源时。基于储能的反馈控制器可以补偿微电网的不希望动力学以提高其稳定性。但是，受到巨大干扰的微电网的最佳反馈控制需要解决汉密尔顿 - 雅各比 - 贝尔曼问题。本文提出了一种基于机器学习的最佳反馈控制方案。它的训练数据集是由线性二次调节器和蛮力方法分别解决的，分别解决了大小的干扰。然后，从数据中构建了三层神经网络，以实现最佳反馈控制。基于修改的昆德两区域系统的微电网模型进行了一个案例研究，以测试提出的控制方案的实时性能。
Microgrids have more operational flexibilities as well as uncertainties than conventional power grids, especially when renewable energy resources are utilized. An energy storage based feedback controller can compensate undesired dynamics of a microgrid to improve its stability. However, the optimal feedback control of a microgrid subject to a large disturbance needs to solve a Hamilton-Jacobi-Bellman problem. This paper proposes a machine learning-based optimal feedback control scheme. Its training dataset is generated from a linear-quadratic regulator and a brute-force method respectively addressing small and large disturbances. Then, a three-layer neural network is constructed from the data for the purpose of optimal feedback control. A case study is carried out for a microgrid model based on a modified Kundur two-area system to test the real-time performance of the proposed control scheme.
