Prediction Model and Application of Turbine Regulating Stage Pressure Under Variable Conditions

Yun LUO; Xuelin CHEN; Ruidong LI; Yongjian SU; Yiwei XU; Junkai CHAO; Pengzhu LI; Haibin REN

doi:10.12096/j.2096-4528.pgt.18173

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Prediction Model and Application of Turbine Regulating Stage Pressure Under Variable Conditions

Power Generation and Environmental protection | 更新时间：2025-06-06

- Prediction Model and Application of Turbine Regulating Stage Pressure Under Variable Conditions
- Power Generation Technology Vol. 40, Issue 2, Pages: 161-167(2019)
- 作者机构：
  
  1.宁夏京能宁东发电有限责任公司, 宁夏回族自治区银川市 750001
  2.北京东方国信科技股份有限公司, 北京市朝阳区 100012
  3.北京京能电力股份有限公司, 北京市朝阳区 100025
- 作者简介：
- 基金信息：
- DOI：10.12096/j.2096-4528.pgt.18173
  CLC：
- Received：11 December 2018，
  
  Published：30 April 2019
- 稿件说明：
移动端阅览
Yun LUO, Xuelin CHEN, Ruidong LI, et al. Prediction Model and Application of Turbine Regulating Stage Pressure Under Variable Conditions[J]. Power Generation Technology, 2019, 40(2): 161-167.
DOI：

Yun LUO, Xuelin CHEN, Ruidong LI, et al. Prediction Model and Application of Turbine Regulating Stage Pressure Under Variable Conditions[J]. Power Generation Technology, 2019, 40(2): 161-167. DOI： 10.12096/j.2096-4528.pgt.18173.

摘要

为监视汽轮机通流部分的健康状况和进行故障早期诊断，以汽轮机变工况计算为理论基础，基于弗留格尔公式和Weierstrass逼近定理，建立了调节级压力的多元回归模型。根据某660MW间接空冷机组大修后的运行数据对模型进行回归分析和验证，结果表明：模型具有很高的拟合度，自变量对因变量影响显著；稳态工况时，预测结果与实测值趋势一致，相对误差约为1.2%，实现了变工况下调节级压力软测量。将模型应用于电厂分布式控制系统中，建立了汽轮机通流部分故障的预警系统。

Abstract

Using the calculation of the variable conditions for steam turbines as the theoretical basis

a multivariate regression model for regulating pressure was established based on the Frugell formula and the Weierstrass approximation theorem. According to the historical operation data of a 660MW generating set

the model was subjected to regression analysis and verification. The results show that the model has a high degree of fit

and the independent variable has a significant influence on the dependent variable; When the steady state conditions are met

the predicted results are consistent with the measured values and the relative error is about 1.2%

which enable soft pressure measurement of the regulation stage. The model was applied to the distributed control system (DCS) of the power plant

and an on-line warning system was established to adjust the pressure anomaly and the partial flow malfunction in the turbine under variable conditions.

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references

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