Risk Assessment Framework for Intelligent Transmission and Distribution Systems Based on Runtime Validation

How to effectively manage the transmission and distribution system and reduce potential risks is a thought-provoking issue. In response to the above issues, this project plans to build an intelligent risk assessment system for transmission networks based on runtime verification. The framework includes steps such as data collection, data preprocessing, feature extraction, modeling, and risk assessment. Through empirical research on real intelligent transmission and distribution system data, we find that the intelligent risk assessment framework based on runtime verification has significant advantages compared to traditional systems. The risk prediction model adopted in our study exhibited a relatively promising result for the prediction of possible risks in power system. According to our analysis results, the performance of the model is around 96 %-100 % in terms of accuracy, the calculation time of the system is greatly reduced, and the false alarm rate is reduced. Moreover, the system has a high real-time feedback and adjustment capability and a high real-time operation efficiency of the algorithms and models. Considering the complexity of risk management in power transmission and distribution systems, an intelligent risk prediction system based on Runtime Verification was developed in our work. The developed framework integrates data collection, data processing, feature extraction, and model building and has better performance with respect to accuracy, efficiency, and real-time capability; compared to traditional methods. The development of our study has contributed to a novel risk mitigation strategy for transmission and distribution system risk management.