Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Liping Fan
Shenyang University of Chemical Technology

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Fuzzy Sliding Mode Control for a Fuel Cell System Liping Fan; Dong Huang; Minxiu Yan
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 5: May 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fuel cell powered systems have low voltage and high current output characteristics. Therefore, the output voltage of the fuel cell must be stepped up by DC-DC converter. In this paper an integrated mathematical model for proton exchange membrane fuel cell power system with DC-DC converter is described by analyzing the working mechanism of the proton exchange membrane fuel cell and the boost DC-DC converter. Fuzzy sliding mode control scheme is proposed to realize stable output voltage under different loads. Simulation operations are carried out and results are compared with fuzzy control and sliding mode control. It is shown that the use of the proposed fuzzy sliding mode controller can achieve good control effect. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2553
Fault Diagnosis for Fuel Cell Based on Naive Bayesian Classification Liping Fan; Xing Huang; Liu Yi
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 12: December 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Many kinds of uncertain factors may exist in the process of fault diagnosis and affect diagnostic results. Bayesian network is one of the most effective theoretical models for uncertain knowledge expression and reasoning. The method of naive Bayesian classification is used in this paper in fault diagnosis of a proton exchange membrane fuel cell (PEMFC) system. Based on the model of PEMFC, fault data are obtained through simulation experiment, learning and training of the naive Bayesian classification are finished, and some testing samples are selected to validate this method. Simulation results demonstrate that the method is feasible.  DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3695
Co-Authors Dong Huang Liu Yi Minxiu Yan Xing Huang