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Defect diagnosis and fault early warning of communication module in electric energy meters based on multi-dimensional feature fusion machine learning algorithm
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DOI：10.23919/jcc.fa.2025-0450.202604.png)
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Abstract
En 中文
In recent years, with the advancement of computational hardware performance, machine learning algorithms have achieved significant development and widespread application across various fields, and have become deeply embedded in smart grids and communication systems. However, it is important to note that despite the widespread deployment of smart meters in the power system, the lack of reliable intelligent diagnostic, a large number of such electricity meters experiencing communication failures caused by internal topological defects every year. To address this issue, we propose a machine learning-based monitoring and early warning model using multidimensional feature fusion. By integrating more than twenty key features in four categories, including attribute features, operational load features, communication behavior features, and derived combined features—an XGBoost classification algorithm framework is constructed to implement risk early warning for electricity meter communication faults. Validated with data from millions of users, the proposed model achieves an accuracy of approximately 90%, the annual average reduction in power outages caused by communication faults is more than 10,000 hours, and significantly enhances the grid's safety and operational stability.
Keywords:
communication faults
intelligent early warning
machine learning internal topological defects
multi-dimensional features fusion
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