Prediction method for monitoring performance of power plant instruments

摘要

Disclosed is a prediction method for monitoring performance of power plant instruments. The prediction method extracts a principal component of an instrument signal, obtains an optimized constant of a SVR model through a response surface methodology using data for optimization, and trains a model using training data. Therefore, compared to an existing Kernel regression method, accuracy for calculating a prediction value can be improved.

主权项

1. A prediction method for monitoring performance of power plant instruments, comprising:
displaying measurement data in a matrix; normalizing the measurement data into a normalized measurement data set; trisecting the normalized measurement data set into three data sets, wherein the three trisected data sets comprising a normalized training data set, a normalized optimization data set, and a normalized test data set; extracting principal components of each of the three trisected data sets; calculating an optimal constant of a Support Vector Regression (SVR) model, based on the normalized measurement data set and the extracted principal components thereof, to optimize prediction value errors of data for optimization with a response surface method; generating the SVR training model with the optimal constant; obtaining a Kernel function matrix with the normalized test data set as an input and predicting an output value of the SVR training model; and de-normalizing the output value into an original range to obtain a predicted value of a variable, wherein the displaying of the measurement data in the matrix is represented by the following equation:X=[X1,1X1,2…X1,mX2,1X2,2…X2,m⋮⋮⋱MX3⁢n,1X3⁢n,2…X3⁢n,m]=[X1X2…Xm]⁢⁢Xts=[X3⁢i+1,1⁢⁢X3⁢i+1,2⁢⁢…⁢,X3⁢i+1,m]=[Xts⁢⁢1⁢⁢Xts⁢⁢2⁢⁢…⁢⁢Xtsm]⁢⁢Xtr=[X3⁢i+2,1⁢⁢X3⁢i+2,2⁢⁢…⁢,X3⁢i+2,m]=[Xtr⁢⁢1⁢⁢⁢Xtr⁢⁢2⁢⁢…⁢⁢Xtrm]⁢⁢Xop=[X3⁢i+1,1⁢⁢X3⁢i+1,2⁢⁢…⁢,X3⁢i+3,m]=[Xop⁢⁢1⁢⁢Xop⁢⁢2⁢⁢…⁢⁢Xopm] where:
X is a matrix representing the measurement data,Xtr, Xop, and Xts are matrices respectively representing a data set for training, a data set for optimization, and a data set for test,n is a positive integer,m is the number of the power plant instruments, andi=0, 1, 2, . . . , n−1.