Research on Accurate Power Outage Research Method Based on Multi-Source Data

With the great increase of people's demand for electricity, intelligent power system is the future development trend of power enterprises.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> The fast and accurate research and judgment of power failure events has become the key to ensure the reliability of power supply and user satisfaction. This paper discusses the power outage research and judgment method of “multi-source system integrated research and judgment + dynamic correction” based on multi-source data. First, the article analyzes the limitations of traditional methods for power system outage assessment. Second, by combining OGG technology and big data analysis, the article completes the collection and preprocessing of data from multiple sources, including power supply service command platform, electricity consumption information collection system, transformer terminals, distribution automation system, dispatch automation system, and centralized control platform. It then explains the process of cross-verification, deduplication, and dynamic correction for data from different sources based on priority levels. Next, by using a hierarchical star topology, the article establishes a “multi-source system comprehensive assessment + dynamic correction” model for power outage assessment, and uses cross-validation methods to adjust model parameters to improve the model's generalization ability. Finally, through experimental design and result analysis, the article verifies the effectiveness and superiority of the proposed model, and trains the model to improve its accuracy and coverage. The research results not only significantly improve the accuracy and efficiency of power outage information, but also enable full-scale perception of customer-side power outages and proactive research on service risks, providing innovative solutions for the stable operation of the power system.