Intelligent monitoring and early warning method and system for power running state

By comprehensively monitoring the electrical and non-electrical quantities of power equipment, and combining transient and persistent anomaly detection with an accident learning layer, an equipment accident diagram model is generated. This solves the problem of low early warning accuracy caused by the single dimension of power operation status monitoring, and realizes more comprehensive power equipment status monitoring and early warning.

CN122241521APending Publication Date: 2026-06-19TIANJIN JIANGTIAN DATA TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TIANJIN JIANGTIAN DATA TECH CO LTD
Filing Date
2026-03-20
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Current power operation status monitoring relies on a single monitoring dimension, resulting in low accuracy of early warnings and an inability to fully guarantee the safe and stable operation of power equipment.

Method used

By monitoring the sampling sequences of electrical and non-electrical quantities of power equipment in real time, transient and persistent anomalies are detected. Combined with the accident learning layer, multi-dimensional accident prediction is performed, and equipment accident graph models are generated to achieve hierarchical early warning management.

Benefits of technology

This improves the comprehensiveness and accuracy of power equipment operation status monitoring, enables early prevention of accidents, and ensures the safe and stable operation of power equipment.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention provides an intelligent monitoring and early warning method and system for power operation status, relating to the field of power monitoring technology. The method includes: real-time monitoring of power equipment based on predetermined monitoring factors to obtain instantaneous sampling sequences; transient fluctuation anomaly detection of the instantaneous sampling sequences based on the synchronous control parameters of the power equipment; continuous anomaly detection of the instantaneous sampling sequences based on multiple consecutive sampling periods; risk correlation fusion based on the transient anomaly detection results and continuous anomaly detection results; introducing an accident learning layer to perform multi-dimensional accident prediction on the equipment anomaly fusion results; generating an equipment accident graph model; and executing hierarchical early warning management of the power equipment. This invention solves the technical problem of low early warning accuracy due to the single dimension of power operation status monitoring in existing technologies. It effectively improves the comprehensiveness and accuracy of power equipment operation status monitoring and early warning, ensuring the safe and stable operation of power equipment.
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