摘要
为实现机组经济性能在线诊断,将支持向量机(SVM)方法引入电站汽轮机排汽焓在线预测领域。该预测方法很好地建立了电站汽轮机排汽焓特性与相关运行参数之间的复杂关系模型,并考虑到相关运行参数之间的耦合性,具有预测能力强、全局最优及泛化性能好等优点。将该SVM方法分别应用于某200MW机组和300MW机组中,对于200MW机组,经过训练后的SVM模型对检验样本排汽焓进行预报,均方根误差和平均相对误差分别为0．110%和0．101%,相当于反向传播(BP)网络模型的38．87%和38．11%,径向基函数(RBF)网络模型的52．38%和49．75%；同理,对于300MW机组,其均方根误差和平均相对误差分别为0．057%和0．069%,相当于BP网络模型的29．61%和25．45%,RBF网络模型的41．57%和34．97%。结果表明：SVM方法优于BP及RBF神经网络法,能很好地满足预测要求。
In order to diagnose the economic performance of unit online, this paper presents a new algorithm to forecast the exhaust enthalpy in the steam turbine online based on support vector machine （SVM）. This online forecasting method establishes the complicated relation model between the steam turbine exhaust enthalpy and the relative operating parameters, considering the coupling performance of every parameter, and has advantages of high forecasting accuracy, global optima property and more generalized performance. Applying this SVM method to a 200 MW unit and a 300 MW one, the SVM model has been trained to forecast the steam turbine exhaust enthalpy in the test samples set. For the 200 MW unit, the mean square root error and the mean relative error is 0. 110% and 0.101%, being 38.87 and 38.11 percent of those from the BP network model, 52.38 and 49.75 percent of those from the RBF network model, respectively; Simultaneously, for the 300 MW unit, the mean square root error and the mean relative error is 0. 057 % and 0. 069 %, being 29.61 and 25.45 percent of those from the BP network model, 41.57 and 34, 97 percent of those from the RBF network model, respectively. These results show that the SVM method could achieve higher accuracy than the BP and RBF neural networks and prove satisfactory in the forecasting demand very well.
出处
《电力系统自动化》
EI
CSCD
北大核心
2006年第18期77-82,共6页
Automation of Electric Power Systems
关键词
汽轮机
排汽焓
支持向量机
在线预测
steam turbine
exhaust enthalpy
support vector machine
online forecasting
