Présentation PowerPointIDENTIFICATION AND MONITORING OF PEM ELECTROLYSER BASED ON DYNAMICAL MODELLING
Mohamed El Hadi LEBBAL, Stéphane LECŒUCHE
Ecole des Mine de Douai
Département Informatique et Automatique
ICHS07 :2nd International Conference on Hydrogen Safety
San Sebastian, Spain - September 11-13. 2007
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PEM electrolyser modelling
improving the process quality and availability (competitiveness)
ensuring the environment safety (people, equipment, building…)
On-line monitoring and diagnosis scheme
Acquire data from sensors, actuators
Compare the process behavior with those of system models
Detect and isolate faults using FDI (Fault Detection and Isolation) algorithms
In this work, limited to the electrolyser, we propose to
Elaborate a PEM electrolyser dynamical model dedicated to basic monitoring and diagnosis tasks
Estimate the real model parameters through identification approach (by using data acquired from the real system)
Build residuals for achieving a first-level diagnosis
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Actuators faults fa (v u),
Sensors faults fs (w y) and
Electrolyser drifts or faults fm (parameters change)
Using
Fault indicators and decision strategy
Electrolyser
System models
fault indicators
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Oxydation
Reduction
Electrical and thermal behaviors
ΔH = ΔG + T·ΔS
Transport phenomena – Influence of concentration change
Electrode and
Membrane resistors
Water in
Activation loss voltage:
Diffusion loss voltage:
Ohmic loss voltage:
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Laplace
transform
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Several model parameters are unknown / difficult to a priori estimate
Identification algorithms
Thermal model parameters (linear system properties)
where
Measurements coming from a 100Nl/h PEM electrolyser
H2 production 100 [Nl/h] , experiments at (1 atm, T=318 K)
Parameter values :
=0.452; I0 =0.1310-3; =0.04; Ilim =120; and Rmem =3.210-3
Average relative error : 0.32%.
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Identified parameters values
Real and identified thermal model
for U=1.74 and I=24
Average relative error : 0.0045.
High-level Residuals generation
Online detection
For each residual i :
Aij=1 Residual i sensitive to fault j
Aij=0 Residual i insensitive to fault j
if B=Aj fault j is isolated
Example of basic table
Electrolyser is healthy
R1(U,I,T,, , I0 Ilim ,Rmem)
An offset on the actuator current occurs
Current actuator value is deviated
by a fault equal to 0.3 A
Healthy case
Membrane resistor
StL
Conclusions
This work is a first attempt to supervise on-line an PEM electrolyser and need to be improved
The main difficulties are
the highly non linear behaviors
It is necessary to combine different modelling approaches
analytical analysis of the process
parameters estimation through experimental modelling
Fault detection and isolation
Detection performance bounded by the quality of the modelling
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different discrete states, different functioning points
Improve the monitoring approach by:
Adaptive thresholding for fault detection defined according the variance of the parameter estimations
Analysis of fault detectors (residuals) sensitivity for several parameters.
Introduce the prediction of faults that could lead to risks
based on the trend analysis of the residuals and not only on their signatures
requirement of a dynamical decision space
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