Anomaly detection with grid sentinel framework for electric vehicle charging stations in a smart grid environment

Electric vehicle (EV) charging stations on the smart grid are needed to promote electric car adoption and sustainable transportation. The key issues are the lack of continuous monitoring and incident response, difficulty linking smart grid systems with EV charging stations, and security gaps that may not address particular vulnerabilities. Modern security measures are needed to protect the grid from those attacks, which may cause significant disruptions. Machine Learning Empowered Anomaly Detection with Grid Sentinel Framework (AD-GS) is proposed to safeguard electric car charging stations against intrusions. This technology can also detect and respond to suspicious movements dynamically using powerful machine learning algorithms (long short-term memory (LSTM), random forest, and autoencoder models), ensuring safety. The testing findings reveal that the systems are automatically updated to neutralize threats quickly, utilizing dynamic methods to minimize downtime. This method increases smart grid safety and can be applied beyond electric car charging stations. The AD-GS architecture is tested in simulations and shown to be resilient against extraordinary attacks, with no impact on charging station performance. The simulation showed that AD-GS could reduce downtime by implementing quick threat mitigation, improve smart grid response time efficiency by 98.4%, and detect abnormalities with 96.8% accuracy. This framework protects user and operation data 99.2% of the time. Extended AD-GS can monitor more than 500 stations and safeguard distribution networks, substations, and electric car charging stations.