Sistema multiusuário para treinamento em subestações de energia elétrica apoiado por técnicas de realidade virtual

Abstract

Electrical substations are crucial components in the power supply system, responsible for the efficient and safe transmission of electricity. Due to the high levels of voltage and power, the operation and maintenance of these facilities involve significant risks, making training a necessity. Traditionally, this training is conducted in locations geographically separate from the actual substations, relying on documents and theoretical demonstrations, which creates a significant gap between the training environment and the real operational scenario. To reduce these limitations, the primary objective of this work is to develop an immersive Virtual Reality (VR) training platform with multi-user support to simulate operations in electrical substations. The methodology employed included a review of relevant literature, requirement definition, and the development of an immersive VR system. The proposed architecture was divided into three main phases: training request, asset modeling, and training design. The system integrates real-time data with SAGE, Eletrobras' substation supervision system, providing a training environment that closely mirrors real working conditions. The platform was evaluated based on the System Usability Scale (SUS) through practical tests with operators. The tests indicated a high acceptance of the system, highlighting the effectiveness of immersive VR in training for operations in electrical substations. The multi-user application allowed for a collaborative training experience, adequately simulating the dynamics of group work. The analysis of the SUS data revealed that users found the system easy to use and well-integrated with the functionalities offered. It was concluded that the application of immersive VR, combined with multi-user support, significantly enriched the training experience, making it more accessible, safe, and engaging. The positive acceptance suggests that this approach can be adopted as a continuous process and integrated with existing training methods.