  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.
