Improving transmission systems through virtual reality modeling and simulation to enhance design visualization

The maintenance of live-line power distribution networks (LL-PDN) is crucial to maintaining the uninterrupted transmission of electricity, particularly in areas with complicated landscapes or harsh environmental conditions. Traditional maintenance procedures are frequently costly, risky, and time-consuming, with staff working directly on live wires. The use of virtual reality (VR) modeling and simulation, combined with artificial intelligence (AI), provides a novel method for improving the design, planning, and maintenance of such systems. The major goal of this research is to create a VR-based platform that incorporates a Shark Nose Optimizer with Gradient Boosting Decision Trees (Shark-GBDT) algorithms for simulating the behavior of LL-PDN, enabling engineers to visualize, and forecast problems before it happens. It uses VR modeling approaches to rebuild power systems and Shark-GBDT-driven predictive models to predict possible faults or maintenance demands. Key findings show that the VR simulation enhances design and defect detection accuracy, allowing for real-time visualizations that aid decision-making. The proposed method is implemented using Python software. In a comparative analysis, the suggested method is assessed with various evaluation measures such as fault detection accuracy (94%), prediction accuracy (93%), time to identify faults (2100 seconds), maintenance downtime (13,800 seconds) and cost of maintenance (70,000). The Shark-GBDT method improves these simulations by detecting possible faults based on prior performance data, optimizing maintenance scheduling, and reducing downtime. Finally, combining VR modeling with AI in the maintenance of live-line power distribution networks appears to be a potential strategy for improving power transmission system efficiency and safety.