.png){kind=icon}
返回
Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation
.png){kind=icon}
OA
AI
DOI：10.3390/su14042051.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parameter estimation problem. First, we propose a novel algorithm to solve the parameter estimation based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, which uses sparse regression to infer the parameters that best describe the observed data. We then compare its performance against another benchmark algorithm, namely, the physics-informed neural networks (PINN) approach applied to parameter estimation. We perform extensive simulations on IEEE bus systems to examine the performance of the aforementioned algorithms. Our results show that the SINDy algorithm outperforms the PINN algorithm in estimating the power grid parameters over a wide range of system parameters (including high and low inertia systems) and power grid architectures. Particularly, in case of the slow dynamics system, the proposed SINDy algorithms outperforms the PINN algorithm, which struggles to accurately determine the parameters. Moreover, it is extremely efficient computationally and so takes significantly less time than the PINN algorithm, thus making it suitable for real-time parameter estimation. Furthermore, we present an extension of the SINDy algorithm to a scenario where the operator does not have the exact knowledge of the underlying system model. We also present a decentralised implementation of the SINDy algorithm which only requires limited information exchange between the neighbouring nodes of a power grid.
Keyword:
parameter estimation
Sparse Identification of Nonlinear Dynamics
physics-informed neural networks
期刊
IF:
3.3
论文数:
10.7W
被引数:
28.4W
机构
引用论文
Estimating Dynamic Model Parameters for Adaptive Protection and Control in Power System电力系统自适应保护与控制的动态模型参数估计
OAAI
.png){kind=icon}
OAAI分享收藏New Late Ordovician (Early Caradoc) bivalves from the Sierra de Villicum (Argentine Precordillera)
PREAI
分享收藏Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations物理信息神经网络: 一种用于解决涉及非线性偏微分方程的正反问题的深度学习框架OAAI
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分享收藏分享收藏分享收藏
查看更多内容