A coal pipeline blockage early warning method for a thermal power plant

By dynamically adjusting the warning threshold using an electrostatic sensor array and a pattern recognition model, and combining generative adversarial networks and computational fluid dynamics simulation, the problem of high false alarm rate in the coal powder pipeline blockage warning system under multi-coal blending was solved. This enabled autonomous learning and continuous optimization, thereby improving the safety and stability of thermal power plants.

CN121089079BActive Publication Date: 2026-03-17BEIJING ZHUXIN QUECHENG TECH CO LTD
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Patent Information

Application Number
CN202511273881.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-09-08
Publication Date
2026-03-17
Estimated Expiration
2045-09-08

AI Technical Summary

Technical Problem

Existing coal pulverized pipeline blockage early warning systems in thermal power plants suffer from high false alarm rates due to significant differences in the electrical characteristics of different coal types under multi-coal blending conditions, resulting in signal baseline drift and insufficient adaptability of the threshold early warning mechanism.

Method used

An electrostatic sensor array is used to collect charge signals, a coal type feature library is established, the dominant coal type is identified through a pattern recognition model, and the early warning threshold is dynamically adjusted based on real-time charge signal characteristics. Virtual monitoring is performed by combining generative adversarial networks and computational fluid dynamics simulation to achieve estimation of the accumulation state and anomaly detection in the bend area.

Benefits of technology

It can effectively identify the differences in flow characteristics under the blending of multiple coal types, dynamically adjust the early warning threshold, reduce the false alarm rate, improve operation and maintenance efficiency, provide targeted solutions, achieve self-learning and continuous improvement, and ensure the safe and stable operation of power plants.

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Abstract

The application discloses a kind of coal powder pipeline blockage early warning method of thermal power plant, it is related to the field of electric power industry automation and condition monitoring technique, the present application automatically identifies the flow characteristic difference of different coal, and dynamically adjusts early warning threshold, avoid the false alarm caused by coal variety change;Adopt the way of generative adversarial network and computational fluid dynamics simulation combination, realize the virtual monitoring to key area such as elbow pipe, break through the limitation that only can rely on straight pipe section monitoring in the past;Through high-frequency sampling and cumulative sum algorithm, the transient abnormal capture ability is significantly improved, and the precursory characteristics of blockage can be found earlier;The linkage of hierarchical early warning mechanism and disposal suggestion makes the system not only can find problems, but also can provide targeted solution, greatly improves operation and maintenance efficiency, using software algorithm upgrade mainly, maximum utilization of existing hardware resources, reduces the transformation cost and implementation difficulty.
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