Monitoring database-based abnormal early warning method for power transformation equipment in severe weather

By building an abnormal risk characteristic library and decision tree model of electrical equipment in extreme weather, the early warning problem of abnormal events in electrical equipment in extreme weather is solved, and the advance warning and accurate positioning of abnormal electrical equipment is achieved, the risk of the power grid is reduced, and the safe and stable operation of the power grid is ensured.

CN119692970APending Publication Date: 2025-03-25MAINTENANCE BRANCH STATE GRID LIAONING ELECTRIC POWER +1
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Patent Information

Application Number
CN202411680473.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-22
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

Under extreme weather conditions, abnormal events in electrical equipment occur frequently. The existing technology has problems such as poor timeliness, difficulty in positioning abnormalities, long handling procedures, and inadequate preparation of spare parts, which are difficult to effectively warn and deal with.

Method used

By obtaining historical monitoring data, analyzing electrical equipment abnormalities, mining feature quantities, building a database of abnormal risks of electrical equipment under extreme weather, and using a decision tree model to combine weather forecasts, historical abnormal data and alarm frequency to conduct abnormal warnings of electrical equipment.

Benefits of technology

It has achieved advanced warnings for potential abnormalities of electrical equipment in extreme weather, improved the accuracy and timeliness of abnormal positioning, guided grid operation and maintenance companies to reserve spare parts and operation inspection forces in advance, reduced grid risks, and ensured the safe and stable operation of the power grid.

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Abstract

The invention discloses an abnormal early warning method for power transformation equipment in severe weather based on a monitoring database. An active abnormal early warning model for electrical equipment in extreme weather is established. And constructing a monitoring database. And establishing an equipment mapping relationship among the multi-source data, carrying out standardization processing on the multi-modal data, and carrying out data analysis. According to the method, a decision tree based on electrical equipment monitoring data is constructed, electrical equipment risk trend analysis and prediction are performed, electrical equipment risk probability calculation is performed, abnormal early warning of power transformation equipment in severe weather is performed according to a prediction result, and possible abnormal events and equipment positions of the electrical equipment are effectively early warned. Power grid operation and maintenance enterprises are efficiently guided to reserve spare parts and operation and maintenance power in advance, so that risks in a power grid and the influence range of the risks are reduced, and safe and stable operation of the power grid is guaranteed.
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