论文标题
通过机器学习的近似轨迹约束 - 微电网岛和频率约束
Approximating Trajectory Constraints with Machine Learning -- Microgrid Islanding with Frequency Constraints
论文作者
论文摘要
在本文中，我们介绍了一种深入学习的辅助约束编码方法，以解决频率约束微电网调度问题。通过使用神经网络近似系统操作条件和频率NADIR之间的非线性函数，该神经网络接受精确的混合组合公式（MIP）。然后将此公式与调度问题集成在一起，以编码频率约束。凭借神经网络的更强代表能力，除了岛屿成功之外，结果命令还可以在现实环境中确保适当的频率响应。提出的方法在修改后的33节点系统上进行了验证。在计划的命令下使用Simulink中的详细三相模型模拟了具有安全响应的成功岛化。当考虑了风力涡轮机发电机的惯性仿真函数时，我们模型的优势尤其显着。
In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.
