Mixed Algorithm of SINDy and HAVOK for Power System Analysis with Inverter-based Resources

核心概念

Effective integration of SINDy and HAVOK algorithms enhances power system analysis by detecting complex nonlinear dynamics in systems with inverter-based resources.

摘要

Artificial intelligence and machine learning tools are improving power grid operations. The paper introduces a novel algorithm combining Sparse Identification of Nonlinear Dynamics (SINDy) and Hankel Alternative View of Koopman (HAVOK) methods to detect nonlinear dynamics in power systems. The mixed algorithm distinguishes nonlinearity caused by inverter-based resources from synchronous generators, supporting data measure-based analysis. SINDy is a measure-based method for model identification, while HAVOK decomposes chaotic dynamics into linear systems. The combined approach addresses challenges posed by complex multi-scale interactions in power grids with renewables. Case studies demonstrate the algorithm's effectiveness in identifying various nonlinear dynamics accurately.

統計資料

Increase in renewable energy penetration shifts power systems to second-order nonlinearity.
SINDy algorithm used for model identification across disciplines.
HAVOK decomposes chaotic dynamics into linear systems.
Burst sampling reduces data requirements for SINDy in multi-scale systems.

引述

"The mixed algorithm distinguishes nonlinearity caused by inverter-based resources from synchronous generators."
"SINDy offers robustness and potential generalization for identifying key dynamical features."
"HAVOK employs time-delay embeddings to capture latent variables within high-dimensional data."

從以下內容提煉的關鍵洞見

Mixed Algorithm of SINDy and HAVOK for Measure-Based Analysis of Power System with Inverter-based Resources

by Reza Saeed K... 於 arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09536.pdf

深入探究

How can the mixed algorithm be applied to real-world large-scale power system models

The mixed algorithm, combining SINDy and HAVOK decomposition, can be applied to real-world large-scale power system models by first adapting the framework to accommodate the complexity and size of these systems. This involves scaling up the data collection process to handle a vast amount of measurements from various points in the grid. Additionally, implementing parallel computing techniques can enhance the algorithm's efficiency in processing extensive datasets typical of large-scale power systems. Furthermore, integrating real-time data acquisition and analysis capabilities into the algorithm allows for dynamic monitoring and control of the system.

What limitations exist when integrating AI/ML tools into measurement-based power system analysis

When integrating AI/ML tools into measurement-based power system analysis, several limitations need to be considered. One limitation is related to data quality and quantity; inaccurate or insufficient data can lead to biased or unreliable results. Moreover, AI algorithms may struggle with interpretability when dealing with complex multi-scale dynamics in power systems, potentially hindering decision-making processes based on their outputs. Another limitation is computational complexity; as power systems grow larger and more interconnected, traditional ML algorithms may face challenges in handling massive datasets efficiently.

How can the concept of coupled multi-scale dynamics be extended beyond power systems

The concept of coupled multi-scale dynamics can be extended beyond power systems by applying it to other interdisciplinary fields where interactions between different scales play a crucial role. For instance:

In climate science: Understanding how small-scale atmospheric phenomena interact with global climate patterns.
In biology: Investigating how molecular interactions influence cellular behavior at various scales.
In finance: Analyzing how microeconomic factors impact macroeconomic trends.
By extending this concept across disciplines, researchers can gain insights into complex systems' behaviors that emerge from interactions at multiple scales.