Tripping Fault Prediction of Heavy-duty Gas Turbines Based on Improved Particle Filter

被引：0

作者：

Teng W. ^{[1
]}

Han C. ^{[1
]}

Zhao L. ^{[1
]}

Wu X. ^{[1
]}

Liu Y. ^{[1
]}

机构：

[1] Key Laboratory of Power Station Energy Transfer Conversion and System, Ministry of Education, North China Electric Power University, Beijing

来源：

| 1600年 / Chinese Mechanical Engineering Society卷 / 32期

关键词：

Heavy-duty gas turbine; Improved particle filter; Secondary resampling; Tripping fault prediction;

D O I：

10.3969/j.issn.1004-132X.2021.02.009

中图分类号：

学科分类号：

摘要：

Heavy-duty gas turbine was the significant equipment in clear energy, and the vibration level of the shafting system is a visual representation of the operating states. Tripping faults were as a kind of unplanned sudden shutdown triggered by increasing vibrations, which would cause a large impact on the core components of the gas turbine, such as blades and tie rods, resulting in equipment damages. A method for predicting the vibration trend of heavy-duty gas turbines was proposed based on improved particle filter. By analyzing the particle filter, a secondary resampling strategy was proposed to make the improved particle filter more resistant to particle degeneracy and improve the adaptability of particle filter. The improved method was verified in a tripping fault of a 300 MW heavy-duty gas turbine, which shows a superior prediction accuracy of tripping fault time. The proposed approach may guide the control strategy of gas turbines. © 2021, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：188 / 194

页数：6

共 18 条

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