Research on Risk Early Warning and Power Outage Prediction Model in Residential Quarters

With the expansion of the power grid and the development of the power market, power companies are facing challenges in customer service risk. Especially in recent years, large-scale power outages caused by natural disasters, weather abnormalities, power equipment failures and other reasons in some areas of China have fully exposed the shortcomings of the current power system in risk warning and disaster response. These power outages have seriously affected the normal life of residents and may even cause a series of social problems. Improving the prediction accuracy of weather-related outages in the distribution system is crucial for disaster prevention and mitlgation‘!’. Based on this, in order to improve the accuracy of risk prediction, enhance the disaster emergency response ability and reduce the workload of grass-roots units, this study intends to study the risk early warning and power failure prediction model. In urban distribution networks, outages are divided into two categories: planned outages and unplanned fault outages, and this study mainly focuses on the latter, that is, those outages that have not been arranged in advance, approved or notified to users. By adopting the technology of “minimized accurate acquisition + digital system calculation”, combined with OGG technology and big data analysis, we monitor six types of power outage-related abnormal data, predict the number of power outage users and give early warning. At the same time, big data and machine learning technology are used to improve prediction accuracy and develop real-time monitoring and early warning systems. The results of this research are of great significance to improve power reliability, reduce the burden of grassroots, and enhance disaster response capabilities.