基于生成对抗网络的暂态稳定预防控制

Preventive Control for Transient Stability Based on Generative Adversarial Network

针对电力系统暂态稳定预防控制在线计算的复杂性,提出一种基于生成对抗网络的暂态稳定预防控制方法。通过将暂态稳定预防控制建模为样本空间映射问题,该方法利用数据驱动方法训练生成模型,建立从暂态失稳运行空间到暂态稳定运行空间的映射。模型通过调整电网中发电机的有功出力,提高电网的暂态稳定裕度,使电网运行点满足暂态稳定校核的要求。与传统优化建模方法相比,所提方法通过神经网络的前馈推断求解控制策略,无需迭代求解,极大地提高了求解效率。基于新英格兰39节点系统的测试结果验证了所提方法的可行性和有效性。

Aiming at the complexity of on-line computation of transient stability preventive control in power systems,this paper proposes a transient stability preventive control method based on the generative adversarial network.By modeling the transient stability preventive control as a sample space mapping problem,a data-driven method is adopted to establish mapping by a training generative model from the transient instability operation space to the transient stable operation space.The model improves the transient stability margin of the power system by adjusting the active power output of the generators to meet the checking requirements of the transient stability for operation points in the power grid.Compared with the traditional optimization modeling methods,the proposed method solves the control strategy through feedforward inference of the neural network without iteration,which greatly improves the solving efficiency.The test results based on the New England 39-bus system verify the feasibility and effectiveness of the proposed method.