Construction and Optimization of Early Warning System for Frequent Blackouts in Active Distribution Network Based on Machine Learning Algorithm

Given that the stability and reliability of Active Distribution Networks (ADN) are crucial for socioeconomic operations, the issue of frequent power outages urgently needs to be addressed. This study employs machine learning algorithms, particularly a hybrid model combining Long Short-Term Memory networks (LSTM) and Gradient Boosting Trees (GBT), to enhance the accuracy and efficiency of the early warning system. The system architecture design encompasses data collection, data processing, model training, generation of warning information, and emergency response mechanisms. By integrating real-time operation parameters, historical outage records, weather forecast and equipment status monitoring data, the system can assess the outage risk in real time and generate early warning. Experimental results show that the LSTM-GBT fusion model achieves 92% early warning accuracy and the false alarm rate drops to 3.6%, which is significantly better than the single LSTM model. This study not only improves the early warning performance, but also continuously optimizes the model parameters and system configuration through the feedback mechanism, which enhances the adaptability and robustness of the system and provides effective decision support for the safe and stable operation of the distribution network.