.png){kind=icon}
返回
Symbolic Regression for Data-Driven Dynamic Model Refinement in Power Systems
.png){kind=icon}
OA
AI
DOI：10.1109/TPWRS.2020.3033261.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper describes a data-driven symbolic regression identification method tailored to power systems and demonstrated on different synchronous generator (SG) models. In this work, we extend the sparse identification of nonlinear dynamics (SINDy) modeling procedure to include the effects of exogenous signals (measurements), nonlinear trigonometric terms in the library of elements, equality, and boundary constraints of expected solution. We show that the resulting framework requires fairly little in terms of data, and is computationally efficient and robust to noise, making it a viable candidate for online identification in response to rapid system changes. The SINDy-based model identification is integrated with the manifold boundary approximation method (MBAM) for the reduction of the differential-algebraic equations (DAE)-based SG dynamic models (decrease in the number of states and parameters). The proposed procedure is illustrated on an SG example in a real-world 441-bus and 67-machine benchmark.
Keyword:
Mathematical model
Power system dynamics
Heuristic algorithms
Nonlinear dynamical systems
Libraries
Power system stability
Computational modeling
Power system
dynamic model
system identification
nonlinear dynamics
symbolic regression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Population survey of predatory insects in sugarcane in the municipality of União, Piauí state, Brazil, using light trap.png)
Científica
OAAI
CientíficaIF0
OAAI分享收藏Discovering governing equations from data by sparse identification of nonlinear dynamical systems通过非线性动力系统的稀疏识别从数据中发现控制方程OAAI
OAAI分享收藏Performance evaluation of different order fractional Chebyshev filter using optimisation techniques
PREAI
分享收藏分享收藏
查看更多内容