Edge Computational Integrated Improved Data Model for Adjustment and Restoration Strategy for Smart Grid Environment

In the current context, integrated distribution networks of the electricity grid to provide reliability during blackouts. Self-healing systems may keep functioning and reconfigure themselves in response to external breakdowns or attacks by using the efficient strategy of restoration. An intelligent decision-making framework is necessary to ensure the system as a whole can self-reconfigure in the face of external failures and attacks. Incorrect grid modifications can pose a significant threat to the power grid's security and cause delays in network traffic and packet loss. So, in this research, Edge Computational Integrated Improved Data paradigm (EC-IDM) to deal with failures and attacks in real time. By mitigating the consequences of security worries, packet loss, latency, and overcrowded networks, edge computing has greatly increased the smart grid's capacity for efficient operation and communication. Smart Grid's posterior intelligent model (SSPIM) integration enhances the system's functionality and productivity. This research introduces an EC-IDM that can detect blackout scenarios and automatically construct a restoration strategy in real time. In EC-IDM, an enhanced hierarchical coordination mechanism and a local object optimization method are combined to optimize failures and attacks in real time over many time and space axes. Differentiating features of EC-IDM include real-time data analysis, networked simulation, smart decision support, and a wealth of other useful features. The simulation results indicate that the recommended strategy achieves best in security concerns optimizes network congestion delay, and packet loss security, increases efficiency and performance compared to the other standard ways.