Application of thermal parameter soft sensor in power plant

摘要

In order to solve the problem of the invalidation of thermal parameters and optimal running, we present an efficient soft sensor approach based on sparse online Gaussian processes( GP), which is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data to specify the prediction of the GP model. By an appealing parameterization and projection techniques that use the reproducing kernel Hubert space (RKHS) norm, recursions for the effective parameters and a sparse Gaussian approximation of the posterior process are obtained. The sparse representation of Gaussian processes makes the GP-based soft sensor practical in a large dataset and real-time application. And the proposed thermalparameter soft sensor is of importance for the economical running of the power plant.