Distribution Line Loss Diagnosis and Classification Recognition Technology Based on Data-driven and Multi-dimensional Scenario Evaluation System of Power System

Driven by the global energy transition, efficient line loss management has become a key link in achieving energy-saving and loss reduction goals in the power system. In response to the complex and variable line losses in distribution network substations and the insufficient accuracy of traditional identification methods, this paper proposes a diagnosis, classification, and identification technology for substation line losses based on data-driven and multi-dimensional scenario evaluation system for power systems. This technology deeply explores the value of multi-source data such as daily line loss rate, daily input electricity, and daily output electricity, and constructs a dynamic adaptive line loss rate anomaly threshold model, breaking through the limitations of traditional fixed thresholds; At the same time, a multidimensional scenario evaluation system is established from five core dimensions: line loss risk, volatility coefficient, discreteness index, line loss computability, and energy domain clustering in the Taiwan area, to achieve comprehensive and accurate diagnosis and classification of line loss anomalies in the Taiwan area. Compared with the traditional “one size fits all” discrimination method, this technology can more sensitively capture the abnormal characteristics of line losses in different scenarios, significantly improve the accuracy and reliability of anomaly recognition, and provide innovative solutions for fine line loss management in the power system. It has important theoretical significance and practical value for promoting energy conservation and loss reduction in the power grid and achieving green and efficient development.