scholarly journals Effect of Sensor Set Size on Polymer Electrolyte Membrane Fuel Cell Fault Diagnosis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2777 ◽  
Author(s):  
Lei Mao ◽  
Lisa Jackson
Keyword(s):  
Fuel Cell ◽  
Fault Diagnosis ◽  
Polymer Electrolyte ◽  
Polymer Electrolyte Membrane ◽  
Principal Component ◽  
Cell Voltage ◽  
Pem Fuel Cell ◽  
Cell System ◽  
Kernel Principal Component Analysis ◽  
Electrolyte Membrane

This paper presents a comparative study on the performance of different sizes of sensor sets on polymer electrolyte membrane (PEM) fuel cell fault diagnosis. The effectiveness of three sizes of sensor sets, including fuel cell voltage only, all the available sensors, and selected optimal sensors in detecting and isolating fuel cell faults (e.g., cell flooding and membrane dehydration) are investigated using the test data from a PEM fuel cell system. Wavelet packet transform and kernel principal component analysis are employed to reduce the dimensions of the dataset and extract features for state classification. Results demonstrate that the selected optimal sensors can provide the best diagnostic performance, where different fuel cell faults can be detected and isolated with good quality.

scholarly journals Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2531
Author(s):  
Feng Han ◽  
Ying Tian ◽  
Qiang Zou ◽  
Xin Zhang
Keyword(s):  
Fuel Cell ◽  
Fault Diagnosis ◽  
Polymer Electrolyte Membrane ◽  
Pem Fuel Cell ◽  
Cell System ◽  
Support Vector ◽  
Fuel Cell System ◽  
Electrolyte Membrane ◽  
Bee Colony ◽  
Original Dataset

In this work, the possibilistic fuzzy C-means clustering artificial bee colony support vector machine (PFCM-ABC-SVM) method is proposed and applied for the fault diagnosis of a polymer electrolyte membrane (PEM) fuel cell system. The innovation of this method is that it can filter data with Gaussian noise and diagnose faults under dynamic conditions, and the amplitude of characteristic parameters is reduced to ±10%. Under dynamic conditions with Gaussian noise, the faults of the PEM fuel cell system are simulated and the original dataset is established. The possibilistic fuzzy C-means (PFCM) algorithm is used to filter samples with membership and typicality less than 90% and to optimize the original dataset. The artificial bee colony (ABC) algorithm is used to optimize the penalty factor C and kernel function parameter g. Finally, the optimized support vector machine (SVM) model is used to diagnose the faults of the PEM fuel cell system. To illustrate the results of the fault diagnosis, a nonlinear PEM fuel cell simulator model which has been presented in the literature is used. In addition, the PFCM-ABC-SVM method is compared with other methods. The result shows that the method can diagnose faults in a PEM fuel cell system effectively and the accuracy of the testing set sample is up to 98.51%. When solving small-sized, nonlinear, high-dimensional problems, the PFCM-ABC-SVM method can improve the accuracy of fault diagnosis.

scholarly journals Modeling and Analysis of Solar Photovoltaic Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell For Running a Hospital in Remote Area in Kolkata, India

2017 ◽  
Vol 6 (2) ◽  
pp. 181 ◽  
Author(s):  
Kamaljyoti Talukdar
Keyword(s):  
Fuel Cell ◽  
Polymer Electrolyte ◽  
Polymer Electrolyte Membrane ◽  
Pem Fuel Cell ◽  
Solar Photovoltaic ◽  
Modeling And Analysis ◽  
Photovoltaic Modules ◽  
Electrolyte Membrane ◽  
Sunshine Hours ◽  
In Series

The present work consists of the modeling and analysis of solar photovoltaic panels integrated with electrolyzer bank and Polymer Electrolyte Membrane (PEM) fuel cell stacks for running different appliances of a hospital located in Kolkata for different climatic conditions. Electric power is generated by an array of solar photovoltaic modules. Excess energy after meeting the requirements of the hospital during peak sunshine hours is supplied to an electrolyzer bank to generate hydrogen gas, which is consumed by the PEM fuel cell stack to support the power requirement during the energy deficit hours. The study reveals that 875 solar photovoltaic modules in parallel each having 2 modules in series of Central Electronics Limited Make PM 150 with a 178.537 kW electrolyzer and 27 PEM fuel cell stacks, each of 382.372 W, can support the energy requirement of a 200 lights (100 W each), 4 pumps (2 kW each), 120 fans(65 W each) and 5 refrigerators (2 kW each)system operated for 16 hours, 2 hours,15 hours and 24 hours respectively. 123 solar photovoltaic modules in parallel each having 2 modules in series of Central Electronics Limited Make PM 150 is needed to run the gas compressor for storing hydrogen in the cylinder during sunshine hours.  Keywords: Central Electronics Limited, Electrolyzer, PEM, PM 150, Solar photovoltaic. Article History: Received Feb 5th 2017; Received in revised form June 2nd 2017; Accepted June 28th 2017; Available onlineHow to Cite This Article: Talukdar, K. (2017). Modeling and Analysis of Solar Photovoltaic Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell For Running a Hospital in Remote Area in Kolkata,India. International Journal of Renewable Energy Develeopment, 6(2), 181-191.https://dx.doi.org/10.14710/ijred.6.2.181-191

scholarly journals Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

2021 ◽  
Vol 163 ◽  
pp. 113550 ◽  
Author(s):  
E. Tsalapati ◽  
C.W.D. Johnson ◽  
T.W. Jackson ◽  
L. Jackson ◽  
D. Low ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Polymer Electrolyte ◽  
Polymer Electrolyte Membrane ◽  
Cell System ◽  
Fuel Cell System ◽  
Electrolyte Membrane ◽  
Semantic Modelling
Sign in / Sign up

Export Citation Format

Share Document