针对火电机组中某些重要热工参数难以直接测量的问题,本文提出了一种基于相关向量机(RVM)的热工参数软测量方法.采用组合核函数的形式代替传统的单一核函数,采用改进的鲸鱼优化算法(IWOA)优化核函数参数和组合核权重系数,利用优化后的参数和机组实际工况数据建立IWOA-RVM 软测量模型,并采用样本更新策略实现该模型的在线更新.将该模型应用于某600 MW 燃煤机组锅炉参数测量,结果表明:本文提出的IWOARVM软测量模型的烟气含氧量和飞灰含碳量预测结果均方根误差分别为0.085 2和0.088 4,均低于BP 神经网络和支持向量机软测量模型;本文模型所需的相关向量少,模型稀疏性好,测试精度高,具有较强的泛化能力.%Some important thermal parameters of thermal power units are difficult to measure directly, to solve this problem, a soft-measurement method based on correlation vector machine (RVM) was proposed. In this method, the combined kernel function replaces the conventional single kernel function, the improved whale optimization algorithm (IWOA) is used to optimize the kernel function parameters and the combined kernel weight coefficient, and the IWOA-RVM soft measurement model is established by using the optimized parameters and the actual operating conditions data of the unit. Moreover, the sample update strategy is applied to realize online update of the model. Furthermore, the above model was employed to the parameter measurement of a 600 MW coal-fired boiler. The results show that, by using the IWOA-RVM soft measurement model, the root mean square error of the predicted oxygen content in flue gas and carbon content in fly ash is 0.085 2 and 0.088 4, both are lower than that by the BP neural network and support vector machine soft measurement model. The proposed model requires less correlation vector, and has good model sparseness, high test accuracy and strong generalization ability.
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