Power grid equipment risk confirmation method and device, computer device and storage medium

By comprehensively considering multi-dimensional information of power grid equipment and dynamic adjustment evaluation cycles, the shortcomings of existing power grid fault analysis systems in adapting to changes in power grid load are resolved, achieving more accurate risk assessment and stable power grid operation.

CN120995125APending Publication Date: 2025-11-21SHENZHEN POWER SUPPLY BUREAU
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Patent Information

Application Number
CN202511189813.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-25
Publication Date
2025-11-21

AI Technical Summary

Technical Problem

Existing power grid fault analysis systems are ill-suited to the dynamic characteristics of power grid load changes, which limits the accuracy of risk level determination.

Method used

By acquiring the fault identifiers and their generation times of each device in the target power grid, and combining multi-dimensional information such as the number of fault identifiers generated, the total number of devices, and the number of different device types within a preset period, the target risk level of the devices is comprehensively calculated. The degree of correlation between fault identifiers and device type and period is considered, and the assessment period and the clearing of correlation relationships are dynamically adjusted.

Benefits of technology

This enables a more accurate reflection of the actual risk status of power grid equipment, improves the accuracy of risk level determination, and ensures the stable operation of the power grid and the security of power supply.

✦ Generated by Eureka AI based on patent content.
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Abstract

The application relates to a power grid equipment risk confirmation method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: obtaining the fault identifiers of each equipment in a target power grid, and the equipment fault generation time corresponding to each fault identifier; determining the initial risk level of each equipment fault according to the proportion of the generation number of each fault identifier in the total number of equipment; determining the association strength of each equipment fault and equipment according to the number of different equipment generating each fault identifier within a preset period; determining the time periodicity strength of each equipment fault according to the equipment fault generation time; and determining the target risk level of each equipment fault according to the initial risk level of each equipment fault, the association strength of each equipment fault and equipment, and the time periodicity strength of each equipment fault. The method can effectively improve the accuracy of risk level determination.
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