Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data

TL;DR

This paper presents a hybrid AI model combining LSTM and CNN techniques for detecting anomalies in phasor measurement unit data to enhance power grid monitoring and security.

Contribution

It introduces a novel hybrid AI approach utilizing LSTM and CNN for anomaly detection in PMU data, incorporating real and synthetic anomalies for improved accuracy.

Findings

Effective detection of anomalies in PMU data using hybrid AI models.

Improved reliability in power system monitoring through advanced anomaly detection.

Analysis of false data injection impacts on anomaly detection performance.

Abstract

Over the last few decades, extensive use of information and communication technologies has been the main driver of the digitalization of power systems. Proper and secure monitoring of the critical grid infrastructure became an integral part of the modern power system. Using phasor measurement units (PMUs) to surveil the power system is one of the technologies that have a promising future. Increased frequency of measurements and smarter methods for data handling can improve the ability to reliably operate power grids. The increased cyber-physical interaction offers both benefits and drawbacks, where one of the drawbacks comes in the form of anomalies in the measurement data. The anomalies can be caused by both physical faults on the power grid, as well as disturbances, errors, and cyber attacks in the cyber layer. This paper aims to develop a hybrid AI-based model that is based on various methods such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN) and other relevant hybrid algorithms for anomaly detection in phasor measurement unit data. The dataset used within this research was acquired by the University of Texas, which consists of real data from grid measurements. In addition to the real data, false data that has been injected to produce anomalies has been analyzed. The impacts and mitigating methods to prevent such kind of anomalies are discussed.