PEMFCs Model-Based Fault Diagnosis: A Proposal Based on Virtual and Real Sensors Data Fusion
Eduardo Ariza1, Antonio Correcher1, Carlos Vargas-Salgado2
1Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
Sensors (Basel, Switzerland)
|September 9, 2023
Summary
This study introduces a new fault diagnosis algorithm for Proton Exchange Membrane Fuel Cells (PEMFCs). The system accurately detects and isolates 14 common PEMFC faults, enhancing reliability in hydrogen fuel cell systems.
Area of Science:
- Renewable Energy Systems
- Electrochemical Engineering
- System Reliability
Background:
- Proton Exchange Membrane Fuel Cells (PEMFCs) are vital for renewable hybrid systems.
- Reliable fault diagnosis is crucial for optimal performance and damage prevention in PEMFCs.
- Hydrogen fuel cells are key for power generation and storage.
Purpose of the Study:
- To develop and validate a novel model-based fault diagnosis algorithm for commercial hydrogen fuel cells.
- To accurately detect and isolate common faults in PEMFCs.
- To enhance the reliability of hydrogen fuel cell-based power generation systems.
Main Methods:
- Utilized LabView for algorithm development.
- Implemented a model-based approach combining virtual and real sensor data fusion.
- Simulated faults using a validated mathematical model and manipulated input signals.
- Performed statistical analysis of 12 residues to create a fault matrix.
Main Results:
- Successfully identified and isolated 14 distinct PEMFC faults.
- The fault matrix effectively captured unique fault signatures.
- Demonstrated high accuracy in fault detection and isolation.
Conclusions:
- The proposed algorithm significantly enhances the reliability of hydrogen fuel cell systems.
- Effective fault diagnosis prevents performance deterioration and system shutdown.
- The model-based approach offers a robust solution for PEMFC fault management.
