火电厂飞灰含碳量多模型融合软测量方法

针对目前火电厂燃煤锅炉飞灰含碳量测量方法存在时间滞后和精度不高等问题,在对锅炉飞灰含碳量影响因素进行分析的基础上,采用基于多个模型的组合可以提高模型精度和鲁棒性的思想,提出基于支持向量机融合的多模型动态软测量建模方法。该建模方法利用时间序列数据建立模型,每个子模型表达某一工况对输出的估计,各个子模型的预测输出通过SVM方法实现变权数融合。应用火电厂历史数据进行飞灰含碳量软测量建模研究,结果表明该方法能够达到较好的测量效果。

Multi-model Fusion Soft Sensor Method for Thermal Power Plant Carbon Content of Fly Ash

In view of the present fly ash carbon content measurement method for thermal power plant coal-fired boiler exists the problems such as time delay and low precision, on the basis of the boiler fly ash carbon content influencing factors' analysis, using the thought that the combination of multiple models can improve the model accuracy and robustness, we proposed a multi-model dynamic soft sensor modeling method based on support vector machine (SVM) fusion. This modeling method uses the time series data to build a model, each submodel express the estimate of the output in one condition, and each submodel's predicted output realizes the variable weighting fusion through SVM method. We did a modeling study of fly ash carbon content soft sensor, using the historical data of thermal power plant, and the result shows that the method can achieve better measurement effect.