Chrysovalantou Ziogou

Modeling and Experimental Validation of a PEM Fuel Cell System

Abstract This work presents a detailed dynamic model and a model validation study using real data from a Hydrogen Fuel Cell Testing Unit (HFCTU). A parameter estimation technique is employed for the determination of key model parameters and the validation of the overall system behavior is carried out by comparing experimental and simulation results. Data illustrate the transient response of the system during load changes. The model is oriented towards process optimization and control and relies on mass balances and electrochemical equations implemented in the gPROMS™ software environment.

Robust constrained stabilization of a boost DC-DC converter with Lyapunov-based control and piecewise-linear Lyapunov functions

In this paper we describe a new methodology for designing robust and efficient state-feedback control laws for a switched-mode boost DC-DC power converter. The proposed design is adopting the so-called Lyapunov-based control law and attempts to solve the robust constrained stabilization problem under large parameter variations. With the methodology proposed static or switching state-feedback control laws are generated so that a number of further key issues are addressed, i.e. low complexity of the implementation, accurate nonlinear dynamics incorporation, nonconservative handling of hard state and control constraints, robustness to supply voltage variations, set-point and load changes. The control design procedure is based on the generation of controlled invariant polytopes (safety domains) using piecewise-linear Lyapunov functions. The proposed technique is numerically evaluated using the exact switched model of the converter.

Multi-Parametric Model Predictive Control of an Automated Integrated Fuel Cell Testing Unit

Abstract The aim of this work is to present a framework for the design of a constrained multiparametric Model Predictive Control (mp-MPC) strategy for a fully automated integrated Hydrogen Fuel Cell Testing Unit (HFCTU) at CERTH. The underlying process is described by an experimentally validated dynamic model which provided the basis for the control framework. This model was used to derive reduced order state space models based on which an explicit/multi-parametric MPC controller was designed in order to satisfy the load demand, avoid starvation and maintain the temperature at a nominal point. The derived controller was tested offline on several operating conditions and showed an excellent transient and steady-state performance.

Design of an energy decision framework for an autonomous RES-enabled smart-grid network

The objective of this work is to develop a reliable, sustainable and adaptive architecture to implement the energy decisions in a complex networked ecosystem for power production by Renewable Energy Sources (RES). To achieve this objective, the modelling of the involved systems is discussed in order to identify the requirements for energy management in dynamic and distributed environments. Also this work investigates the impact of emerging Internet of Things (IoT) architecture on an energy production networked systems in a smart grid environment. Furthermore, the relation of the developed IoT enabled architecture to the energy management decisions is overviewed combined with a Machine2Machine (M2M) approach with implementation aspects relying on industrial-grade software. Preliminary results are presented that show the response of the proposed scheme to a smart grid system with RES and hydrogen production, usage and storage.

Optimal switching Lyapunov-based control of a boost DC-DC converter

A new methodology for designing robust and efficient state-feedback control laws for a switched-mode boost DC-DC power converter has been recently proposed. This approach has adopted the so-called stabilizing or Lyapunov-based control paradigm which is well-known in the area of energy-based control of DC-DC converters, whereby the control law takes a state-feedback form parameterized by a positive scalar γ. Extension to state-dependent switching state-feedback control laws has been proposed, where the switching surfaces are parameterized by a number of positive scalars γ.i. In this paper this methodology is revisited by considering the problem of designing optimal switching state-feedback control laws, i.e. finding the optimal control parameters γ.i corresponding to the optimal position of the switching surfaces. This permits minimization of the number of switchings required for achieving an optimal performance and hence reduced complexity of the control law. Systematic derivation of gradient information to apply gradient-descent algorithms is provided. The proposed technique is numerically evaluated using the exact switched model of the converter.

Development of a Nonlinear Model Predictive Control Framework for a PEM Fuel Cell System

Abstract The aim of this work is to demonstrate that nonlinear model based predictive control (NMPC) is a suitable approach for the efficient and optimal operation of a PEM fuel cell system. Thus, a NMPC-based framework is developed structured on a simultaneous full-discretization optimization method which involves the use of orthogonal collocation on finite elements and represents the optimal control problem as a nonlinear programming problem (NLP). The purpose of the framework is to control the system behaviour under a range of operating conditions. Also the performance of the overall scheme is fine-tuned so that the resulting controller can be deployed at an on-line industrial automation environment. Results indicate that the developed NMPC framework exhibits excellent performance characteristics in terms of both computational requirements and convergence rates between successive iterations. Furthermore it is illustrated that the system can follow successfully set point changes of the load demand and compensates errors occurring from sudden disturbances in operating conditions.

Optimal switching Lyapunov-based control of power electronic converters

This paper presents new ideas and insights towards a novel optimal control approach for power electronic converters. The so-called stabilizing or Lyapunov-based control paradigm is adopted, which is well known in the area of energy-based control of power electronic converters, in which the control law takes a nonlinear state-feedback form parameterized by a positive scalar λ. The first contribution is the extension to an optimal Lyapunov-based control paradigm involving the specification of the optimal value for the parameter λ in a typical optimal control setting. The second contribution is the extension to more flexible optimal switching-gain control laws, where the optimal switching surfaces are parameterized by a number of positive scalars λj. Systematic derivation of gradient information to apply gradient-descent algorithms is provided. The proposed techniques are numerically evaluated using the exact switched model of a DC-DC boost converter. Copyright

Robust malfunction diagnosis in process industry time series

In this work, a modified version of a Slope Statistic Profile (SSP) method is proposed, capable to detect real-time incidents that occur in two interdependent time series. The estimation of incident time point is based on the combination of their linear trend profiles test statistics, computed on a consecutive overlapping data window. Furthermore, the proposed method uses a self-adaptive sliding data window. The adaptation of the size of the sliding data window is based on real-time classification of the linear trend profiles in constant and equal time intervals, according to two different linear trend scenarios, suitably adjusted to the conditions of the problem we face. The proposed method is used for the robust identification of a malfunction and it is demonstrated to real datasets from a chemical process pilot plant that is situated at the premises of CERTH / CPERI during the evolution of the performed experiments at the process unit.

Human resource optimisation through semantically enriched data

The industrial domain is experiencing a so-called fourth industrial revolution in which the evergrowing complexity of manufacturing information, the increasing amount of knowledge and the use of web-oriented techniques, represent three crucial factors that are accelerating the growth of complexity of industrial systems. On the other hand, continuous-evolving requirements in industrial environments, due to technology outbreaks and a new global marketplace, have led to an on-going evolution of human resource management through the creation and adoption of alternative business models. In the past decade, semantic models such as ontologies have been proven to be effective for many knowledge-intensive applications, since they provide formal models of domain knowledge that can be exploited in different ways. For all these reasons, an innovative human resource optimisation (HRO) engine is introduced, which employs semantically enhanced information and conditional random field (CRFs) probabilistic models with knowledge derived from industrial shop floor level, and proposes the right person for the right job in real-time shop floor operations towards optimising decisions on how to implement and schedule either repeatedly or non-occurring tasks. Industrial information data flow and semantic enrichment were ensured through the combined use of a common interface data exchange model (CIDEM) and ontologies, after which a feasibility study at a chemical plant presented interesting preliminary results.

Model-Based Predictive Control of Integrated Fuel Cell Systems—From Design to Implementation

Fuel cell systems are a promising alternative to traditional power sources for a wide range of portable, automotive and stationary applications and have an increasing potential for wider use as the demand for clean energy is increasing and the focus is shifting towards renewable energy generation. This chapter has a multidisciplinary scope, the design of a computer-aided framework for monitoring and operation of integrated fuel cell systems and the development of advanced model-based control schemes. The behavior of the framework is experimentally verified through the online deployment to an automated small-scale fuel cell unit, demonstrating excellent response in terms of computational effort and accuracy with respect to the control objectives.