反向建模方法在火电厂关键参数建模中的应用

Application of Reversed Modeling Method in Power Plant Critical Parameters Modeling

提出在火电厂关键参数建模中采用反向建模方法,以规避传统建模方法在实际应用中的建模难题.以超临界直流锅炉中间点温度为例,利用某600MW超临界机组的实际运行数据,采用反向建模方法建立了该参数的数学模型.建模算法选用最小二乘支持向量机(LS-SVM),应用粒子群算法(PSO)解决了LS-SVM参数寻优问题,并将PSO-LS-SVM所得模型与LS-SVM、偏最小二乘(PLS)以及BP神经网络所得模型进行了对比,结果表明:基于PSO-LS-SVM的中间点温度数学模型计算速度快、精度高,验证了反向建模思想的有效性和可行性.

Reversed modeling method was proposed to avoid the difficulty of the traditional modeling method in power plant critical parameters modeling. Based on the actual operation data of a 600 MW supercrifical once-through boiler, mathematic model of the intermediate point temperature was constructed by reversed modeling method. The modeling algorithm was least square support vector machines （LS-SVM）, and particle swarm optimization （PSO） was used to solve the LS-SVM optimal parameters question. A comparison has been made among models respectively obtained by PSO-LS-SVM, LS-SVM, partial least square（PLS）, BP artificial neural networks（BP-ANN）. Results show that with the intermediate point temperature model based on PSO-LS-SVM, faster and accurate calculation can be achieved, proving the reversed modeling method to be effective and feasible.