IDENTIFICATION AND MONITORING OF PEM ELECTROLYSER BASED ON DYNAMICAL MODELLING

1. ICHS07 :2nd International Conference on Hydrogen Safety San Sebastian, Spain - September 11-13. 2007 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

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

3. Introduction • Supervision of H2 production stations for • 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

4. Problem formulation • Detection and isolation of electrolyser faults • Actuators faults fa (v u), • Sensors faults fs(w y) and • Electrolyser drifts or faults fm (parameters change) • Using • Input/Output measurements u,y • Electrolyser model (giving an estimate of the output) • Fault indicators and decision strategy fs fa fm y fault indicators u v w Monitoring and diagnosis Sensors Electrolyser Actuators System models

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

6. Electrical modelling (1/2) Electric energy Cathode (-) Anode (+) Reduction e- Oxidation e- Vrev reversible voltage Vdiff Diffusion voltage Vact activation voltage Vohm Ohmic voltage Water in Hydrogenout e- e- H+ H2O Transport phenomena – Influence of concentration change Electrode and Membrane resistors At equilibrium thermodynamic Voltage when I=0+ Chemical reaction velocity Charge movement near to electrodes H2 O2 Oxygen out Hydrogenout Membrane Electrodes • Voltage losses U: Cell voltage

7. Electrical modelling (2/2) • Voltage expression • Electrical model U=f(I) Reversible voltage: Activation loss voltage: Diffusion loss voltage: Ohmic loss voltage: V0=1.23, R, F, I0, Ilim, Rmem, PH2, PO2, aH2O,  and  : constants

8. Thermal modelling • Thermal behaviour (Busquet 2004) with Vth=1.48,  Cp, and h: constants, Ta  : Ambient temperature. Temperature variation Reaction heat External Flow Let definex=T-Ta, u=(U-Vth)Iandy=T-Ta Laplacetransform Basic model of order 1

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

10. Electrical model parameters identification • Non linear least square method • 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 =0.1310-3;  =0.04; Ilim =120; and Rmem =3.210-3 Average relative error : 0.32%. Real and identified electrical model

11. Thermal model parameters identification • Step identification • Estimation static gain and response time of linear system • Identified parameters values • at Ta=298°K, Cp=68544 and h=10.71 Real and identified thermal model for U=1.74 and I=24 Average relative error : 0.0045.

12. Online monitoring and diagnosis fs fa fm • Model based Monitoring and diagnosis • High-level Residuals generation Real system Uk Ik R1 Sensors Actuator Electrolyser Tk R2 Monitoring and diagnosis Using electrical model Rj Using thermal model Electrolyser modelling  0  0

13. Drift or fault detection and isolation • Definition (off-line) of a signature table • Online detection • Update of the vector of residuals B • For each residual i : • Decision according to the signature table : Aij=1  Residualisensitive to faultj Aij=0  Residualiinsensitive to fault j Example of basic table if Ri> Threshold then Bi=1; else Bi=0. if B=Aj fault j is isolated

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

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

16. Conclusions • This work is a first attempt to supervise on-line an PEM electrolyser and need to be improved • The main difficulties are • the variety of physical phenomena to be modelled • 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 • 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

17. The next steps • Improve the modelling by using a multi-modelling representation • 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