论文标题
用于建模和预测微电网瞬变的模块化双线koopman操作员
Modularized Bilinear Koopman Operator for Modeling and Predicting Transients of Microgrids
论文作者
论文摘要
在存在干扰的情况下，将模块化的Koopman双线性形式（M-KBF）提出了模型和预测微电网的瞬态动力学。作为一种可扩展的数据驱动方法，M-KBF将高维非线性系统的识别和预测划分为子系统的个别研究。因此，减轻了严格处理大量数据并克服维度的诅咒的困难。对于每个子系统，Koopman双线性形式用于通过通过扩展的动态模式分解方法开发特征性的函数，并具有基于特征值的订单截断，以有效地识别其模型。广泛的测试表明，M-KBF可以为非线性微电网提供准确的瞬态动力学预测，并验证插件建模和预测功能，该功能为识别高维系统提供了有效的工具。 M-KBF的模块化功能可以为微电网操作和控制提供快速，精确的预测，从而为在线应用程序铺平了道路。
Modularized Koopman Bilinear Form (M-KBF) is presented to model and predict the transient dynamics of microgrids in the presence of disturbances. As a scalable data-driven approach, M-KBF divides the identification and prediction of the high-dimensional nonlinear system into the individual study of subsystems; and thus, alleviating the difficulty of intensively handling high volume data and overcoming the curse of dimensionality. For each subsystem, Koopman bilinear form is applied to efficiently identify its model by developing eigenfunctions via the extended dynamic mode decomposition method with an eigenvalue-based order truncation. Extensive tests show that M-KBF can provide accurate transient dynamics prediction for the nonlinear microgrids and verify the plug-and-play modeling and prediction function, which offers a potent tool for identifying high-dimensional systems. The modularity feature of M-KBF enables the provision of fast and precise prediction for the microgrid operation and control, paving the way towards online applications.
