论文标题
使用可区分参数优化的神经物理动态载荷建模
Neuro-physical dynamic load modeling using differentiable parametric optimization
论文作者
论文摘要
在这项工作中，我们研究了一种数据驱动的方法，该方法用于获得分布系统的减少等效负载模型，用于机电瞬态稳定性分析。提出的减少等效物是一种神经物理模型，该模型包括传统的拉链载荷模型，该模型通过神经网络增强。这种神经物理模型通过可区分的编程进行培训。我们讨论了设置为差异参数程序的提出模型的公式，建模细节和培训。该神经物理拉链负载模型的性能和准确性在中等规模的350-BUS传输 - 分布网络上呈现。
In this work, we investigate a data-driven approach for obtaining a reduced equivalent load model of distribution systems for electromechanical transient stability analysis. The proposed reduced equivalent is a neuro-physical model comprising of a traditional ZIP load model augmented with a neural network. This neuro-physical model is trained through differentiable programming. We discuss the formulation, modeling details, and training of the proposed model set up as a differential parametric program. The performance and accuracy of this neurophysical ZIP load model is presented on a medium-scale 350-bus transmission-distribution network.
