Integration of digital twin technologies in smart grid substations

Abstract

Abstract In the era of Industry 5.0, digital transformation of smart grid substations is crucial to address growing demands for efficiency and reliability. However, conventional substations lack real-time monitoring. Fault detection takes a long time, and predictive maintenance is substandard. In this research, an entirely new approach is proposed to transform substation work processes by combining Digital Twin (DT) technologies with an algorithm based on Gradient Boosting Machine (GBM) designs. Within the confines of a digital twin technology, a substation can begin to operate, and hence a virtual representation, which can be termed, an operating digital twin of a substation can be created ready for real time, predictive fault diagnosis, and active asset management. The solution proposed reduces total operational downtime by 30% and increases fault detection performance by 25%, the results supported by the following performance metrics Precision 94%, Recall 91%, and F1-score 92%. Analysis performed in the MATLAB simulations proves that the substations adaptive control systems can maintain the stability and reliability of the electricity grid system with variable loads and environmental conditions, thereby allowing the creation of advanced smart substations which are more efficient and reliable.