基于PSO-SVM的快速甩负荷工况下过热蒸汽温度预测建模
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摘要
结合海防电厂300MW机组100%负荷下的快速甩负荷试验,采用粒子群优化算法(PSO)对支持向量机(SVM)的参数进行优化,并建立PSO-SVM过热蒸汽温度预测模型。在分析试验时机组燃料量、高旁阀位开度及温度等主要参数变化过程的基础上,对过热蒸汽温度预测模型进行仿真分析。结果表明:建立的PSO-SVM过热蒸汽温度模型具有较高的预测精度,能够实现快速甩负荷工况下过热蒸汽温度的预测,维持机组安全稳定运行。
Combining with the FCB tests under a 300 MW unit's 100% load conditions in Haifang Power Plant,making use of PSO algorithm to optimize SVM parameters and to establish a PSO-SVM-based prediction model of the superheated steam was implemented. Having the changing process of main parameters like the unit's fuel amount,valve opening and the temperature analyzed and the prediction model of the superheated steam temperature simulated shows that this PSO-SVM-based prediction model has higher accuracy in quickly predicting the superheated steam temperature under FCB conditions and it can maintain the unit's safe and stable operation.
Combining with the FCB tests under a 300 MW unit's 100% load conditions in Haifang Power Plant,making use of PSO algorithm to optimize SVM parameters and to establish a PSO-SVM-based prediction model of the superheated steam was implemented. Having the changing process of main parameters like the unit's fuel amount,valve opening and the temperature analyzed and the prediction model of the superheated steam temperature simulated shows that this PSO-SVM-based prediction model has higher accuracy in quickly predicting the superheated steam temperature under FCB conditions and it can maintain the unit's safe and stable operation.
引文
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[2]张明.煤制天然气项目甲烷化废热锅炉系统的技术探讨[J].化工机械,2012,39(2):121~123,161.
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[4]黄宝海,韩璞,葛忠真,等.基于现场数据的电站过热器神经网络建模[J].华北电力大学学报,2008,35(2):66~69.
[5]于红霞.基于神经网络过热汽温模型辨识[J].锅炉制造,2008,(4):66~68.
[6]Sun Z H,Sun Y X.Fuzzy Support Vector Machine for Regression Estimation[C].IEEE International Conference on Systems,Man and Cybernetics.Washington:IEEE,2003:3336~3341.
[7]Chen R,Liang C Y,Hong W C,et al.Forecasting Holiday Daily Tourist Flow Based on Seasonal Support Vector Regression with Adaptive Genetic Algorithm[J].Applied Soft Computing,2015,26:435~443.
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[11]Kennedy J,Eberhart R.Particle Swarm Optimization[J].IEEE International Conference on Neural Networks,1995,4(8):1942~1948.
[12]孙灵芳,邵萌萌,刘旭颖.基于改进的BP算法的过热汽温建模[J].自动化技术与应用,2010,29(4):1~3.