Title :
Fault Diagnosis of Power Electronic Circuit Based on Random Forests Algorithm and AR Model
Author :
Ren-Wu, Yan ; Jin-Ding, Cai
Author_Institution :
Electr. Eng. & Automatization Coll., Fuzhou Univ., Fuzhou, China
Abstract :
This paper presents a novel method of applying auto-regressive(AR) model and random forests to fault diagnosis of power electronic circuit. AR model is used to extract the features of the sample data, realize optimum compressed of fault sample data, simplify the data structure in fault diagnosis, enhance classify speed and precision. By simulating fault status of power electronic circuit, this paper investigates design details of random forests classifier and evaluates its performance. Experimental results show that the method is feasible and effective.
Keywords :
autoregressive processes; fault diagnosis; power electronics; random processes; auto-regressive model; fault diagnosis; power electronic circuit; random forests algorithm; Circuit faults; Educational institutions; Fault diagnosis; Feature extraction; Kernel; Neural networks; Power electronics; Power system reliability; Support vector machine classification; Support vector machines; AR model; fault diagnosis; power electronic circuit; random forests;
Conference_Titel :
Information and Computing Science, 2009. ICIC '09. Second International Conference on
Conference_Location :
Manchester
Print_ISBN :
978-0-7695-3634-7
DOI :
10.1109/ICIC.2009.79
