Identification of abnormal operation of electric energy metering equipment based on multi-dimensional and multi-layer association rule mining algorithm
Year: 2024
Type: conference-paper
Abstract: In order to comprehensively and deeply explore the potential correlations and patterns in operational data, this paper proposes a method for identifying abnormal operation of electric energy metering equipment based on multi-dimensional and multi-layer association rule mining algorithms. A multi-level association rule mining model has been constructed to automatically discover potential correlations and abnormal patterns between different indicators, thereby accurately identifying abnormal operation of energy metering equipment. The raw running data is converted into a form suitable for association rule mining, and evaluation metrics such as support and confidence are calculated for each rule. Abnormal operation of energy metering equipment is identified based on these evaluation indicators. The experimental results show that it can effectively identify the abnormal operation of energy metering equipment, providing strong technical support for equipment maintenance and troubleshooting of power companies.
Authors: Jianfeng Sun, Yang Gao, Cunyu Long, Shubei Hua
Institution: State Grid Corporation of China (China)
Language: English
Topic: Smart Grid and Power Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405257412