Validation of Neural Network-based Fault Diagnosis for Multi-stack Fuel Cell Systems: Stack Voltage Deviation Detection☆

Abstract This paper presents (i) an algorithm for the detection of unexpected stack voltage deviations in an Solid Oxide Fuel Cells (SOFC)-based power system with multiple stacks and (ii) its validation in a simulated online environment. The algorithm is based on recurrent neural networks (RNNs) and is validated by using operating data from the Wartsila WFC20 multi-stack SOFC system. The voltage deviation detection is based on statistical testing. Instead of a hardware implementation in the actual power plant, the algorithm is validated in a simulated online environment that provides data I/O communication based on the OPC (i.e. Object Linking and Embedding (OLE) for Process Control) protocol, which is also the technology utilized in the real hardware environment. The validation tests show that the RNN-based algorithm effectively detects unwanted stack voltage deviations and also that it is online-viable

On the use of neural networks and statistical tools for nonlinear modeling and on-field diagnosis of solid oxide fuel cell stacks

Abstract The paper reports on the activities performed within the European funded project GENIUS to develop black-box models for modeling and diagnosis of solid oxide fuel cell (SOFC) stacks. Two modeling techniques were investigated, i.e. Neural Networks (NNs) and Statistical Tools (STs). The deployment of NNs was twofold: Recurrent Neural Networks (RNNs) and an NN classifier were developed to simulate transient operation of SOFCs and identify some specific faults that may occur in such devices, respectively. On the other hand, STs are based on a stepwise multiple regression. Data for model development were obtained from experiments specifically designed to reach maximal information content. The final aim was to obtain highly general models of SOFC stacks' operation in both transient and steady state. All the developed black-box models exhibited high accuracy and reliability on both training and test data-sets. Moreover, the black-box models were also proven effective in performing real-time monitoring and degradation analysis for different SOFC stack technologies

Microvia Superfilling Process Control

Electrochemical thermodynamics is the foundation of the microvia fill process, whose model represents the mass conservation of species (copper ions and surfactants) in the condition of moving boundary. The shape of via changes due to the deposed metallic copper, whose growth depends on the blocking effect of surfactants, which in turn depends on the boundary curvature. The via filling process is controlled through the boundary by adjusting the electric current flowing through the bath system. Maximizing the current raises the via fill process speed but ends up in depletion of the copper ions at the bottom of via causing an incomplete via fill (poorly fabricated board). In this paper the problem is solved by adjusting the concentration of copper ions to a reference value near the panel surface of the plated board. The stabilizing control is proposed based on the developed via fill model. By applying the control, the via fill process can be sped up by ca. 20% and the via dimple minimized 5% without risking product output quality.Peer reviewe

A Lumped Dynamic Modelling Approach for Model-Based Control and Diagnosis of Solid Oxide Fuel Cell System with Anode Off-Gas Recycling

This work focuses on the identification and validation of a Solid Oxide Fuel Cell (SOFC) stack model that accounts for anode off-gas recycling (AOGR) inclusion. A lumped dynamic modelling approach is adopted to simulate temperature and gas composition at the stack anode outlet. The model is able to simulate the dynamic response of the stack during transients. Experimental data from a real SOFC system are used in model validation and the modeling approach adopted here ensures achieving a satisfactory compromise between estimation accuracy and computational burden. These factors support using the model as state estimator in model-based control and diagnosis algorithms