Application of Computer Virtual Reality Technology in Power System Training and Simulation System

An innovative Seagull Optimization Term Memory (SOTM) model comes into being in the study of computer virtual reality technology applied to power system training and simulation. The model effectively integrates the strengths of both the Long Short-Term Memory (LSTM) network and the Seagull Optimization Algorithm (SOA), resulting in the creation of the SOTM model. This innovative design aims to augment the precision and effectiveness of power system training and simulation platforms, furnishing power engineering professionals with advanced, highly-realistic training utilities and more dependable power system simulation outcomes. LSTM networks are very suitable for simulating dynamic behavior and trend changes of power systems because of their ability to capture and remember long-term dependencies when processing time series data. In the power system training and simulation system, LSTM can learn and predict the state change of the power system based on the historical operation data, so as to realize the accurate simulation of the complex behavior of the power system. However, the selection and optimization of LSTM model parameters are crucial to the performance of the model, especially in the face of high-dimensional, nonlinear and dynamically changing complex systems such as power systems, which may be difficult to find the optimal solution by traditional training methods. To this end, the researchers introduced the seagull optimization algorithm. As an optimization algorithm derived from natural phenomena, SOA draws on the efficient search strategy shown by seagulls when they forage for food, and has strong global search capability and fast convergence speed. In the context of the SOTM model, the Seagull Optimization Algorithm (SOA) is employed to refine and optimize the parameters within the LSTM model, thereby enabling it to align more precisely with the operational principles of the power system. By integrating the dual advantages of LSTM and SOA, SOTM model not only significantly improves the simulation accuracy of dynamic behavior in the process of power system training and simulation, but also improves the learning and optimization efficiency of the model. In practical applications, the SOTM model can provide power engineers with a more realistic and interactive virtual reality training environment, helping to improve their operational skills and emergency response capabilities. Simultaneously, the SOTM model also demonstrates remarkable promise in real-time simulation, fault anticipation, and decision-making assistance within power systems, thereby offering robust technological backing for the intelligent management and optimized functioning of power systems.