Knowledge Graph and LSTM-Based Risk Identification Framework for Relay Protection in Power Systems: Fault Detection, Fault Location Estimation and Predictive Analytics

In the active landscape of 6G wi-fi systems, combining knowledge graph technology and advanced data analysis strategies presents a transformative approach to monitoring and managing relay protection status in intelligent substations. This paper implements a risk identification framework for relay protection equipment operation in smart substations. Based on this, the paper proposes an innovative Knowledge Graph (KG)-based risk identification model that effectively combines the remote tracking abilities of KG with advanced predictive and analytical ability to ensure efficient, accurate and reliable control over substation equipment. Our model facilitates real-time data collection and exchange by combining interconnected devices with sensors, actuators and network connectivity, improving substations’ operational performance and risk management. The LSTM component was also introduced, which is well-suited for analyzing the time series data and also applied to monitor operational patterns, detect anomalies and predict the future risk of failures. Simultaneously, the KG-based system is effectively used for local fault cases to identify risky equipment and become aware of fault paths within the substation’s network. This approach effectively minimizes human intervention by automating the monitoring process and significantly reducing the dependence on traditional, costly solutions. The proposed design consists of three key modules — KG-LSTM-FD for fault detection, KG-LSTM-RI for risk identification and KG-LSTM-FL for fault location estimation. Our model proved exceptional sensitivity and reliability, achieving a 99.98% successful rate throughout diverse fault scenarios with location estimation errors within 1%. The KG-LSTM-based risk identification model, examined through a prototype system, marks a tremendous advancement in combining the physical world with computer-based systems, providing a cost-effective, highly efficient and scalable solution for the next generation of smart substations.