基于支持向量回归的300MW电站锅炉再热汽温建模

为解决某电厂300MW电站锅炉再热汽温异常的问题,提出一种基于支持向量回归的建模方法,采用现场数据进行数据建模。建立在数据统计特性基础上的模型具有高的回归相关度,能反映出再热汽温与操作参数之间的内在联系。针对机组存在的再热器出口汽温偏低而部分管壁温度过高的问题进行了回归分析,结果表明模型具有较高的相关系数,且模型复杂度较低,具有好的鲁棒性。作为现场试验辅助手段,对进一步进行参数优化和再热汽温调节具有重要指导意义和参考价值。

Modeling Research of the Reheat Steam Temperature of 300 MW Boiler Based on Support Vector Regression

To solve the problem of reheat steam temperature (RST) abnormal of the 300MW power station boiler unit, a method based on support vector regression (SVR) was presented to model RST. Based on the data sampled on spot, RST was analyzed using support vector regression method. RST model is based on the statistical characteristics of the operating parameters and can reflect the potential relationship between RST and the operating parameters. For the units considered here, the RST is low and the temperature of reheater tube wall is high, tilt angles and desupreheater spray, etc. were taken as the tuning parameters and as the features of SVR model. The prediction results on test data with SVR-RST show high regression coefficient with low complexity, which means SVR-RST model has excellent robustness, which is important to further optimize the operating parameters for higher efficiency and security.