1 StL IDENTIFICATION AND MONITORING OF PEM ELECTROLYSER BASED ON DYNAMICAL MODELLING Mohamed El Hadi LEBBAL, Stéphane LECŒUCHE Ecole des Mine de Douai Département Informatique et Automatique Laboratory : Informatics and control system ICHS07 :2 nd International Conference on Hydrogen Safety San Sebastian, Spain - September

2 StL Presentation outline Context of the work Context of the work Improvement of the availability of an hydrogen station Improvement of the availability of an hydrogen station Development of tools dedicated to the predictive maintenance Development of tools dedicated to the predictive maintenance PEM electrolyser modelling PEM electrolyser modelling Electric and thermal models Electric and thermal models Parameters estimation Parameters estimation Identification using IO data Identification using IO data Monitoring and diagnosis Monitoring and diagnosis Fault detection and isolation Fault detection and isolation Conclusions and Perspectives Conclusions and Perspectives

3 StLIntroduction Supervision of H2 production stations for Supervision of H2 production stations for improving the process quality and availability (competitiveness) improving the process quality and availability (competitiveness) ensuring the environment safety (people, equipment, building…) ensuring the environment safety (people, equipment, building…) On-line monitoring and diagnosis scheme On-line monitoring and diagnosis scheme Acquire data from sensors, actuators Acquire data from sensors, actuators Compare the process behavior with those of system models Compare the process behavior with those of system models Detect and isolate faults using FDI (Fault Detection and Isolation) algorithms Detect and isolate faults using FDI (Fault Detection and Isolation) algorithms In this work, limited to the electrolyser, we propose to In this work, limited to the electrolyser, we propose to Elaborate a PEM electrolyser dynamical model dedicated to basic monitoring and diagnosis tasks 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) Estimate the real model parameters through identification approach (by using data acquired from the real system) Build residuals for achieving a first-level diagnosis Build residuals for achieving a first-level diagnosis

4 StL Problem formulation Detection and isolation of electrolyser faults Detection and isolation of electrolyser faults Actuators faults f a  (v  u), Actuators faults f a  (v  u), Sensors faults f s  (w  y) and Sensors faults f s  (w  y) and Electrolyser drifts or faults f m  (parameters change) Electrolyser drifts or faults f m  (parameters change) Using Using Input/Output measurements u,y Input/Output measurements u,y Electrolyser model (giving an estimate of the output) Electrolyser model (giving an estimate of the output) Fault indicators and decision strategy Fault indicators and decision strategy Electrolyser Monitoring and diagnosis y u w v fafa fsfs fmfm Sensors Actuators System models fault indicators

5 StL Water in H+H+ H2H2 H2OH2O O2O2 Oxygen out Hydrogen out e-e- e-e- Oxidation  e - Reduction  e - Anode (+) Cathode (-) Electric energy Membrane Electrodes 2H 2 O + electric energy  2H 2 + O 2 2H 2 O  O 2 + 4H e - Oxydation Reduction 4H e -  2H 2 PEM electrolyser PEM electrolyser principle PEM electrolyser principle

6 StL Based on the Functional equation (Electrochemical conversion) Based on the Functional equation (Electrochemical conversion) Electrical and thermal behaviors Electrical and thermal behaviors ΔH = ΔG + T·ΔS Enthalpy change Total energy change Gibbs energy change Electrical demand Thermal energy Heat demand Cell Current I Cell voltage U Cell temperature T Entropy reaction Components temperature Thermal model I, U T Electrolyser Modelling Electrical model

7 StL U: Cell voltage V rev reversible voltage V act activation voltage V diff Diffusion voltage V ohm Ohmic voltage At equilibrium thermodynamic Voltage when I=0 + Chemical reaction velocity Charge movement near to electrodes Transport phenomena – Influence of concentration change Electrode and Membran e resistors Electrical modelling (1/2) Voltage losses Voltage losses Water in H+H+ H2H2 H2OH2O O2O2 Oxygen out Hydrogen out e-e- e-e- Oxidation  e - Reduction  e - Anode (+) Cathode (-) Electric energy Membrane Electrodes

8 StL Voltage expression Voltage expression Electrical model U=f(I) Electrical model U=f(I) Reversible voltage: Activation loss voltage: Diffusion loss voltage: Ohmic loss voltage: V 0 =1.23, R, F, I 0, I lim, R mem, P H2, P O2, a H2O,  and  : constants Electrical modelling (2/2)

9 StL with Vth=1.48, C p, and h: constants, T a : Ambient temperature. Temperature variation Reaction heat External Flow Let define x=T-T a, u=(U-V th )I and y=T-T a Laplace transform Thermal modelling Thermal behaviour (Busquet 2004) Thermal behaviour (Busquet 2004) Basic model of order 1

10 StL Parameters identification Several model parameters are unknown / difficult to a priori estimate Several model parameters are unknown / difficult to a priori estimate Identification algorithms Identification algorithms Electrical model parameters (NLS non-linear least squares) Electrical model parameters (NLS non-linear least squares) Thermal model parameters (linear system properties) Thermal model parameters (linear system properties) where

11 StL Average relative error : 0.32%. Real and identified electrical model Electrical model parameters identification Non linear least square method Non linear least square method Measurements coming from a 100Nl/h PEM electrolyser Measurements coming from a 100Nl/h PEM electrolyser  H2 production 100 [Nl/h], experiments at (1 atm, T=318 K) Parameter values : Parameter values :  =0.452; I0 =0.13  10-3;  =0.04; Ilim =120; and Rmem =3.2   =0.452; I0 =0.13  10-3;  =0.04; Ilim =120; and Rmem =3.2  10 -3

12 StL Real and identified thermal model for U=1.74 and I=24 Average relative error : Thermal model parameters identification Step identification Step identification Estimation static gain and response time of linear system Estimation static gain and response time of linear system Identified parameters values Identified parameters values at Ta=298°K, Cp=68544 and h=10.71 at Ta=298°K, Cp=68544 and h=10.71

13 StL Model based Monitoring and diagnosis Model based Monitoring and diagnosis High-level Residuals generation High-level Residuals generation Electrolyser Electrolyser modelling TkTk IkIk Sensors Actuator UkUk Using electrical model Using thermal model Real system fafa fsfs fmfm  0 Online monitoring and diagnosis Monitoring and diagnosis R2R2 R1R1 RjRj

14 StL A ij =1  Residual i sensitive to fault j A ij =0  Residual i insensitive to fault j if B=A j  fault j is isolated Example of basic table Electrolyser is healthy Thermal part is faulty Electrical part is faulty Sensors or actuators are faulty R1(U,I,T, , , I 0 I lim,R mem ) 0011 R2(U,I,T, a, b) 0101 if  Ri  > Threshold then B i =1; else Bi=0. Drift or fault detection and isolation Definition (off-line) of a signature table Definition (off-line) of a signature table Online detection Online detection Update of the vector of residuals B Update of the vector of residuals B For each residual i : For each residual i : Decision according to the signature table : Decision according to the signature table :

15 StL Current actuator value is deviated by a fault equal to 0.3 A Healthy case Signature Experiments (1/2) Healthy case vs Actuator fault Healthy case vs Actuator fault An offset on the actuator current occurs An offset on the actuator current occurs

16 StL h thermal parameter deviated by a value equals to (10) Membrane resistor deviation equals to 10% Signature Experiments (2/2) Electrolyser faults Electrolyser faults

17 StLConclusions This work is a first attempt to supervise on-line an PEM electrolyser and need to be improved This work is a first attempt to supervise on-line an PEM electrolyser and need to be improved The main difficulties are The main difficulties are the variety of physical phenomena to be modelled the variety of physical phenomena to be modelled the highly non linear behaviors the highly non linear behaviors It is necessary to combine different modelling approaches It is necessary to combine different modelling approaches analytical analysis of the process analytical analysis of the process parameters estimation through experimental modelling parameters estimation through experimental modelling Fault detection and isolation Fault detection and isolation Residuals designed according the electrical or thermal behavior Residuals designed according the electrical or thermal behavior  Detection performance bounded by the quality of the modelling Several residuals need to be defined in order to isolate faulty components Several residuals need to be defined in order to isolate faulty components

18 StL The next steps Improve the modelling by using a multi-modelling representation Improve the modelling by using a multi-modelling representation different discrete states, different functioning points different discrete states, different functioning points Improve the monitoring approach by: Improve the monitoring approach by: Adaptive thresholding for fault detection defined according the variance of the parameter estimations Adaptive thresholding for fault detection defined according the variance of the parameter estimations Analysis of fault detectors (residuals) sensitivity for several parameters. Analysis of fault detectors (residuals) sensitivity for several parameters. Introduce the prediction of faults that could lead to risks Introduce the prediction of faults that could lead to risks based on the trend analysis of the residuals and not only on their signatures based on the trend analysis of the residuals and not only on their signatures requirement of a dynamical decision space requirement of a dynamical decision space