Integration of Physics- and Data-Driven Power System Models in Transient Analysis After Major Disturbances
Integration of Physics- and Data-Driven Power System Models in Transient Analysis After Major Disturbances
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DOI：
10.1109/jsyst.2022.3150237
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发表时间：
2023-03
影响因子：
4.4
通讯作者：
A. A. Sarić-A.;M. Transtrum;A. Sarić;A. Stanković
中科院分区：
文献类型：
--
作者：
A. A. Sarić-A.;M. Transtrum;A. Sarić;A. Stanković
本文从物理模型和数据驱动模型的交错、协调和改进等方面探讨了大型电力系统在大扰动下的暂态现象分析。重大干扰可导致级联故障，最终导致部分电力系统停电。我们的主要兴趣是在一个框架中，它能够在工程系统中协调和无缝地集成使用两种类型的模型。该框架的部分内容包括：1)优化的压缩感知，2)定制的Koopman算子有限维近似，以及3)物理驱动（基于方程）和数据驱动（基于深度神经网络）模型的灰盒集成。将所提出的三阶段方法应用于441总线多机测试系统的暂态稳定性分析，并给出了在接点有局部测量的同步发电机的暂态稳定性分析结果。
The article explores the analysis of transient phenomena in large-scale power systems subjected to major disturbances from the aspect of interleaving, coordinating, and refining physics- and data-driven models. Major disturbances can lead to cascading failures and ultimately to the partial power system blackout. Our primary interest is in a framework that would enable coordinated and seamlessly integrated use of the two types of models in engineered systems. Parts of this framework include: 1) optimized compressed sensing, 2) customized finite-dimensional approximations of the Koopman operator, and 3) gray-box integration of physics-driven (equation-based) and data-driven (deep neural network-based) models. The proposed three-stage procedure is applied to the transient stability analysis on the multimachine benchmark example of a 441-bus real-world test system, where the results are shown for a synchronous generator with local measurements in the connection point.