Thermal power plant electrical equipment overload self-adaptive early warning system

By introducing an adaptive early warning system into the electrical equipment of thermal power plants, dynamic perception of equipment overload risks, multi-source information fusion, and graded response are achieved, solving the static and one-sided problems of existing early warning systems and improving the safety and reliability of equipment operation.

CN121563199APending Publication Date: 2026-02-24北方联合电力有限责任公司包头第一热电厂
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Patent Information

Application Number
CN202511625625.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-07
Publication Date
2026-02-24

AI Technical Summary

Technical Problem

Existing overload early warning systems for electrical equipment in thermal power plants are unable to dynamically adapt to changes in equipment operating conditions due to fixed thresholds and lack of self-learning capabilities. This leads to frequent false alarms and missed alarms, making it impossible to accurately assess the health status of equipment. Furthermore, the lack of multi-source data fusion and hierarchical response mechanisms affects the safe operation of equipment.

Method used

The system employs a data acquisition and fusion module, a dynamic threshold calculation module, a risk probability assessment module, and a graded early warning decision module. Combined with an adaptive learning algorithm, it generates overload early warning thresholds that match the equipment's operating conditions in real time. The model is then optimized through a feedback learning module to achieve multi-source data fusion, risk quantification assessment, and graded response.

Benefits of technology

This improved the adaptability and accuracy of the early warning system, reduced the false alarm rate, enabled early identification and timely response to equipment overload risks, and enhanced the safety and reliability of equipment operation.

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Abstract

The invention provides a self-adaptive early warning system for overload of electrical equipment of a thermal power plant. The self-adaptive early warning system comprises a data acquisition and fusion module, a dynamic threshold calculation module, a risk probability evaluation module, a graded early warning decision module and a feedback learning and model updating module. The data acquisition and fusion module acquires multi-dimensional operation parameters of the electrical equipment in real time and forms an equipment state vector; the dynamic threshold calculation module generates a dynamic early warning threshold through an adaptive learning algorithm based on historical data and real-time working conditions; the risk probability evaluation module calculates a quantitative risk probability in combination with the real-time parameter deviation and the equipment health degree; the grading early warning decision module executes grading response from visual prompt to load control according to the risk grade; and the feedback learning module continuously optimizes algorithm parameters. According to the method, the limitation of traditional fixed threshold early warning is broken through, dynamic threshold self-adaption, accurate risk quantification, early warning grading response and continuous model optimization are realized, the early warning accuracy is remarkably improved, and the false alarm rate is effectively reduced.
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