Low Voltage Substation Line Loss Analysis and Phase Identification Based on Big Data and Intelligent Algorithms

The analysis and phase identification of line loss anomalies are of core significance in power grid planning and loss reduction strategy construction. These two tasks not only help power companies quickly detect and solve hidden problems in grid operation, but also provide key data support for optimizing grid architecture and improving energy efficiency. However, some regions face obstacles in this critical task due to a lack of phase separation data, making it difficult to quickly locate the fault location or comprehensively evaluate the line loss status of the substation, which in turn has a negative impact on the safe and reliable operation of the power grid and economic benefits. To solve this problem, this article introduces a novel method: a low-voltage substation line loss analysis and phase recognition algorithm based on big data and artificial intelligence (AI) technology. This algorithm combines the advantages of big data processing capabilities and AI algorithms, with the goal of accurately analyzing line losses in the substation area and quickly identifying phase information. Practical verification has shown that the algorithm has achieved significant results in actual deployment, not only significantly improving the accuracy of fault location, but also deeply analyzing the root causes of line loss in the substation area, laying a solid foundation for adopting efficient loss reduction measures.