用神经网络统一建模发电机与电力电子设备动态特性
A Unified Approach for Learning the Dynamics of Power System Generators and Inverter-based Resources
- 采用带稳定积分的RNN捕捉设备动态递归关系
- 在含同步机和逆变器的小系统中预测准确率高
- 可快速计算参数变化对动态的影响，适合电网仿真
随着可再生能源接入和电气化进程中逆变器型资源（IBRs）的广泛使用，电力系统动态分析面临挑战。本文提出一种学习单个动态元件模型的统一方法，利用循环神经网络（RNN）匹配从端口电压和设定值输入预测关键动态状态的递归结构。为应对逆变器引起的快速暂态，开发了稳定积分（SI-）RNN，模拟高阶积分方法，提升动态学习任务的稳定性和精度。数值验证基于包含同步发电机（SGs）和IBRs的小型测试系统的全阶电磁暂态（EMT）仿真，结果表明该模型不仅能准确预测元件动态行为，还可高效计算设定值变化下的动态灵敏度，尤其适用于电网型逆变器的动态预测。
原文摘要 · Abstract (English)
The growing prevalence of inverter-based resources (IBRs) for renewable energy integration and electrification greatly challenges power system dynamic analysis. To account for both synchronous generators (SGs) and IBRs, this work presents an approach for learning the model of an individual dynamic component. The recurrent neural network (RNN) model is used to match the recursive structure in predicting the key dynamical states of a component from its terminal bus voltage and set-point input. To deal with the fast transients especially due to IBRs, we develop a Stable Integral (SI-)RNN to mimic high-order integral methods that can enhance the stability and accuracy for the dynamic learning task. We demonstrate that the proposed SI-RNN model not only can successfully predict the component's dynamic behaviors, but also offers the possibility of efficiently computing the dynamic sensitivity relative to a set-point change. These capabilities have been numerically validated based on full-order Electromagnetic Transient (EMT) simulations on a small test system with both SGs and IBRs, particularly for predicting the dynamics of grid-forming inverters.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。