Title :
Fault detection in Kerman combined cycle power plant boilers by means of support vector machine classifier algorithms and PCA
Author :
Berahman, M. ; Safavi, A.A. ; Shahrbabaki, M.R.
Author_Institution :
Kerman combined cycle power plant, Kerman, Iran
Abstract :
In this paper, fault detection in HP drum of boilers in Kerman combined cycle power plant is explored by means of support vector machine (SVM) algorithm and principal component analysis (PCA). Initially, SVM classifier algorithm and PCA are discussed and then based on the collecting data on normal and abnormal operating the conditions of boilers, fault detection is carried out via explained methods. Finally, a comparison of these techniques and other routine methods is made to show the superiority with the proposed approaches in Kerman power plant.
Keywords :
boilers; combined cycle power stations; fault diagnosis; pattern classification; power engineering computing; power generation reliability; principal component analysis; support vector machines; Kerman combined cycle power plant boiler HP drum; PCA; SVM classifier algorithm; data collecting; fault detection; principal component analysis; support vector machine algorithm; Boilers; Classification algorithms; Fault detection; Indexes; Power generation; Principal component analysis; Support vector machines;
Conference_Titel :
Control, Instrumentation, and Automation (ICCIA), 2013 3rd International Conference on
Conference_Location :
Tehran
DOI :
10.1109/ICCIAutom.2013.6912851