Leveraging Digital Twin and Machine Learning Techniques for Anomaly Detection in Power Electronics Dominated Grid

TL;DR

This paper presents a novel approach combining digital twin technology and machine learning to detect anomalies and cyber threats in power electronics-dominated grids, enhancing grid security and stability.

Contribution

It introduces an integrated digital twin and machine learning framework specifically designed for real-time anomaly detection in modern power grids.

Findings

Effective real-time anomaly detection demonstrated

Improved early warning capabilities for cyberattacks

Enhanced grid stability and cybersecurity

Abstract

Modern power grids are transitioning towards power electronics-dominated grids (PEDG) due to the increasing integration of renewable energy sources and energy storage systems. This shift introduces complexities in grid operation and increases vulnerability to cyberattacks. This research explores the application of digital twin (DT) technology and machine learning (ML) techniques for anomaly detection in PEDGs. A DT can accurately track and simulate the behavior of the physical grid in real-time, providing a platform for monitoring and analyzing grid operations, with extended amount of data about dynamic power flow along the whole power system. By integrating ML algorithms, the DT can learn normal grid behavior and effectively identify anomalies that deviate from established patterns, enabling early detection of potential cyberattacks or system faults. This approach offers a comprehensive and proactive strategy for enhancing cybersecurity and ensuring the stability and reliability of PEDGs.