Study on Application of Multi-Kernel Relevance Vector Machine in Data Validation of Thermal System in Power Plant

Abstract

A new method for data validation of thermal process in power plant was proposed based on multi-kernel relevance vector machine (MKRVM). Hybrid kernel function combining Gaussian kernel and polynomial kernel function was applied to relevance vector machine (RVM). After optimizing kernel parameters with firefly algorithm, nonlinear data regression model was built for thermal system. Then we used measuring test method to detect data and reconstruct data with model prediction. This method can overcome multiple correlation and nonlinearity among variables of thermal system. It has good robustness against various types of noise and higher accuracy for prediction compared with SVM and RVM. The results of case analysis for thermal system in a 600 MW unit show this method can detect and reconstruct data effectively.