A Machine Learning-Based Smart Grid Protection and Control Framework Using Kalman Filters for Enhanced Power Management

Recent years have seen an increase in electrical crises due to the proliferation of automated inductors and electrical applications such as electric vehicles and mobile devices.The greater dispersion in the smart grid exposes it to risks like cyber-attacks, attenuation, and faulty detections that were not prevalent in conventional methods.The proposed machine learning-based renewable energy smart grid protector and controller (ReSGPC) using Kalman filters effectively controls and detects noise faults, cyberattacks, and attenuation, addressing the mentioned problems.Additionally, the proposed method has increased the efficiency of the smart grid due to its superior performance compared to conventional methods.This method provides an additional layer of protection for the system, safeguarding grid information.An optimal control law is developed to ensure the stability of the power network.The controller demonstrates significant improvements in effectiveness regardless of the initial values.Numerical simulations verify the developed approach, showing that the recommended method offers a more powerful line of attack.This strategy provides a crucial energy management framework for the smart grid, representing a reliable and system-based communication infrastructure with applications integrating renewable resources.Performance analysis reveals substantial improvements, with the proposed method achieving an efficiency increase of 0.25%, 0.42%, 0.32%, and 0.34% in Mean Squared Error (MSE) for ∆1, ∆2, ∆3, and ∆4 scenarios respectively, compared to existing methods.