Research on Intelligent Diagnostic Techniques for Line Loss Based on Comprehensive Analysis Utilizing Machine Learning Methodologies

In the context of the expanding power grid infrastructure and escalating electricity demand, the issue of line loss has emerged as a significant concern within the power industry. This paper endeavors to investigate an intelligent diagnostic technique for line loss, leveraging comprehensive analysis through machine learning methodologies, with the objective of enhancing the precision, flexibility, and adaptability of line loss diagnosis. By developing models for the classification of non-conforming characteristics of transformer districts, the analysis of primary factors contributing to non-compliant line loss, the examination of correlated anomaly causes, and the assessment of quantifiable impacts of anomalies, and by incorporating sophisticated machine learning paradigms such as the SHAP (SHapley Additive exPlanations) algorithm, Bayesian networks, deep learning, and reinforcement learning, this paper presents a comprehensive framework for the quantitative evaluation and optimal mitigation of anomalies in line loss within transformer districts.