Research on power grid outage risk assessment and early warning model based on intelligent decision algorithm

Electricity is transmitted from generators to consumers through an interconnected system of transmission lines known as a power grid. There are outages if there are problems with the network. Rules-based techniques and statistical analysis, used in conventional power outage prediction and fault detection approaches, are typically inadequate for modeling power systems’ complex patterns and dynamics. Identifying power line outages rapidly and accurately is crucial to the continuous operation of smart grids. Aiming at the scheduling issues for regional power grids under power uncertainties and outages, this paper suggests a Deep Learning-assisted Risk Assessment and Early Warning Model (DL-RAEWM) for power grid outage detection with intelligent decision-making. The information is taken from the power outage data to analyze the power line outage risk. Based on computer vision methods and deep neural networks, visual inspections of the electrical power system may be automated to increase the network’s reliability. The power systems data, such as voltage levels and load distributions, can be effectively analyzed using Deep Neural Networks (DNNs) with spatial dependencies. The simulation outcomes show the model has good convergence, intelligent decision-making, and robustness.