Research on Data-driven Intelligent Diagnosis Technology for Distribution Network Operation with High Renewable Energy Penetration

Joint Uzbek-China project.

Abstract

As the global energy transition accelerates and the penetration of renewable energy increases, distribution networks face challenges like rising equipment failure rates and reduced operational efficiency. The rapid integration of renewables adds complexity, making traditional diagnostic methods less effective and heightening failure risks, especially in areas with weak infrastructure. Current research on intelligent diagnostic systems for distribution networks is limited, particularly regarding large-scale renewable integration. Thus, there’s an urgent need for new technologies to enhance automation and safety within these networks. This project adopts a data-driven approach framed by “data fusion - data modeling - algorithm design - system development.” By innovating through multimodal data fusion, dynamic topology generation, and advanced diagnostic algorithms, it aims to achieve comprehensive monitoring of distribution network operations and rapid, precise fault diagnosis. This will provide essential technical support for the efficient use of renewable energy and the stable, secure operation of power systems.