Integration of equation- and signal-based models in transient analysis of electric energy systems
Integration of equation- and signal-based models in transient analysis of electric energy systems
复制标题
电能系统瞬态分析中基于方程和信号的模型的集成
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
A. Stanković
中科院分区：
文献类型：
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作者：
A. Sarić;A. Stanković
本文讨论了基于方程的（经典）和信号派生的[人工神经网络（ANN）]动态模型在大规模动力系统暂态分析中的集成的分析和实践方面。我们的基于信号的部分是基于两个人工神经网络，并从边界点的测量中得出。在本文中，我们描述了这种混合建模技术，并重点关注：1)基于最小二乘的基于实际运行条件的动态变量预测在线校正机制；2)该算法对通信链路故障导致的测量缺失的恢复能力；3)基于微分代数方程的子系统与基于人工神经网络的子系统之间的完全双向交互。本文通过将基于人工神经网络的模型与Matlab中的暂态分析工具箱相结合，证明了在标准电力系统软件中实现该方法的可行性。我们通过一个来自新英格兰/纽约互联电力系统的基准多机示例说明了所提出方法的暂态分析能力。
The paper addresses analytical and practical aspects of integration of equation-based (classical) and signal-derived [artificial neural network (ANN)] dynamic models for transient analysis of large-scale dynamical systems. Our signal-based part is based on two ANNs, and is derived from measurements at boundary points. In this paper, we describe this hybrid modeling technique, and focus on: 1) a least square-based mechanism for on-line correction of dynamic variable predictions that is based on actual operating conditions; 2) the resilience of the algorithm to missing measurements due to failed communication links; and 3) a complete two-way interaction between the differential-algebraic equation based subsystem and the ANN-based subsystem. The paper demonstrates the feasibility of implementing our approach in standard power system software by integrating the ANN-based model with the transient analysis toolbox from Matlab. We illustrate capabilities of the proposed approach for transient analysis on a benchmark multi-machine example derived from the New England/New-York interconnected power system.
影响因子：
6.6
作者：
Burth, M;Verghese, GC;Vélez-Reyes, M
通讯作者：
Vélez-Reyes, M