热力发电

面向深度调峰的磨煤机智能诊断与自愈控制系统研发及应用

PDF下载

热力发电 | ,():

收起

热力发电 |

面向深度调峰的磨煤机智能诊断与自愈控制系统研发及应用

全屏

作者信息

通讯作者:

作者简介:

Research and application of intelligent diagnosis and self-healing control system for coal mills under deep peak shaving

Affiliations

Corresponding Author:

doi: 10.19666/j.rlfd.202607061

文章导航

摘要

收起

【目的】针对电力调度深度快速变负荷与入炉煤质波动共同作用下，燃煤机组制粉系统频繁变负载运行，易诱发堵磨、断煤故障异常，传统单点固定阈值报警难以准确报出，故障诊断与闭环处置滞后等问题，提出了一种面向堵磨与断煤异常工况的磨煤机智能诊断与自愈控制方法。

【方法】基于磨煤机物料平衡、风粉输运和电机负载特性建立关键状态参数模型，引入非线性状态观测器，实现磨内原煤存储量和煤粉存储量等不可测状态的在线估计，并结合差压、电流、出口温度和给煤机瞬时煤量构建了堵磨、断煤故障异常工况判据，基于模糊量化方法构建堵磨、断煤综合故障指数，建立早期预警与故障预警分级机制，设计堵磨和断煤自愈顺控流程，开发了基于云边协同架构的磨煤机故障诊断与自愈闭环控制系统。

【结果】应用表明，系统能够在异常发展初期识别堵磨和断煤趋势，并联动煤量、冷热风调节及振打装置投运等措施实现故障自愈；其中堵磨自愈过程约10 min，断煤自愈过程约5 min，处置过程中机组主要运行参数扰动较小，磨煤机关键参数可平稳恢复至正常范围。

【结论】研究成果可为电力调度与煤质波动双向扰动下，煤电机组制粉系统智能诊断、运行风险抑制和少人化控制提供工程参考。

Abstract

收起

[Objective] Under deep and rapid load changes driven by power dispatch and coal quality fluctuations, pulverizing systems of coal-fired units frequently operate under wide-range variable loads, easily inducing coal blockage and coal interruption faults. These faults are concealed and evolve gradually with hysteretic observable parameters, so traditional fixed-threshold DCS alarms cannot provide accurate early warning, and diagnosis and closed-loop disposal lag behind. To address these problems, an intelligent diagnosis and self-healing control method for coal mill blockage and interruption abnormal conditions is proposed.

[Methods] For a medium-speed mill positive-pressure direct-fired pulverizing system, a key state parameter model is built on material balance, air-coal transport and motor load characteristics, describing the dynamic coupling among internal material inventories, differential pressure and mill current. A nonlinear state observer with prediction-correction feedback is introduced to estimate the unmeasurable internal inventories online via the measured mill current residual, and model parameters are identified from healthy operating data by the particle swarm optimization algorithm. Diagnostic criteria integrating virtual material states, model residuals and parameter directionality are then constructed based on differential pressure, current, outlet temperature and instantaneous coal feed rate. Four feature quantities are fused through fuzzy quantification into a continuous comprehensive fault index, and a hierarchical warning mechanism comprising early warning and fault warning is established. Self-healing sequential control procedures for the two faults are designed, with maintenance personnel notification and manual intervention links introduced; a cloud-edge coordinated closed-loop diagnosis and self-healing control system is developed, with modules deployed on a hyper-converged server and linked to the DCS via OPC UA and Modbus TCP.

[Results] Validation on a 4 h independent data set shows that the reconstructed current and differential pressure track DCS measurements well, with RMSE of 1.12 A and 0.04 kPa and mean relative errors of 1.9% and 0.72%. Field application on a 1 000 MW ultra-supercritical unit shows that the system identifies blockage and interruption trends at the early fault stage and achieves self-healing by coordinating coal feed adjustment, hot/cold air regulation and vibration device activation. The blockage and interruption self-healing processes take about 10 min and 5 min; the disturbance to main unit parameters is small, and key mill parameters are smoothly restored to normal ranges. A model degradation monitoring and parameter online updating mechanism based on sliding residual windows further ensures long-term model effectiveness.

[Conclusion] The proposed method supports the transformation from post-fault alarm to early trend prompting and immediate disposal, providing a reference for intelligent diagnosis, risk suppression and reduced-manpower operation of pulverizing systems under bidirectional disturbances from power dispatch and coal quality.

引用本文

. 面向深度调峰的磨煤机智能诊断与自愈控制系统研发及应用. 热力发电, : - . DOI: 10.19666/j.rlfd.202607061

. Research and application of intelligent diagnosis and self-healing control system for coal mills under deep peak shaving[J]. Thermal Power Generation, : - . DOI: 10.19666/j.rlfd.202607061

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

年第卷第期

PDF下载

122

引用本文

BibTeX

文章信息

doi: 10.19666/j.rlfd.202607061

首发时间：2026-10-16

补充材料

相关文章

文章信息

作者

出版历史

基金

作者信息

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/rlfd/CN/10.19666/j.rlfd.202607061

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

关闭全屏

BibTeX
EndNote
RefWorks
TxT