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Neural Model-Based Self-Tuning PID Strategy Applied to PEMFC

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Engineering（科研） 2014年第4期6卷 159-168页

作者： Cédric Damour Michel Benne brigitte grondin-perez Jean-Pierre Chabriat Physics Department University of La Reunion Saint-Denis France

This paper illustrates the benefits of a self-tuning PID strategy applied to a proton exchange membrane fuel cell system. Controller parameters are updated on-line, at each sampling time, based on an instantaneous linearization of an artificial neural network model of the process and a General Minimum Variance control law. The self-tuning PID scheme allows managing nonlinear behaviors of the system while avoiding heavy computations. The applicability, efficiency and robustness of the proposed control strategy are experimentally confirmed using varying control scenarios. In this aim, the original built-in controller is overridden and the self-tuning PID controller is implemented externally and executed on-line. Experimental results show good performance in setpoint tracking accuracy and robustness against plant/model mismatch. The proposed strategy appears to be a promising alternative to heavy computation nonlinear control strategies and not optimal linear control strategies.

关键词： Self-Tuning PID Controller Artificial Neural Network Model Proton Exchange Membrane Fuel Cell Real-Time Control Scheme Experimental Implementation

Energy Efficiency Improvement of an Industrial Crystallization Process Using Linearizing Control

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Journal of Crystallization Process and Technology 2012年第2期2卷 44-54页

作者： Cédric Damour Michel Benne Lionel Boillereaux brigitte grondin-perez Jean-Pierre Chabriat GEPEA ONIRIS Nantes France. Laboratory of Energetics Electronics and Processes University of La Reunion Saint-Denis France Laboratory of Energetics Electronics and Processes University of La Reunion Saint-Denis France 2GEPEA ONIRIS Nantes France.

This paper illustrates the benefits of a multivariable linearizing control approach applied to an industrial crystallization process. This relevant approach is declined according to two different strategies: first, a setpoint tracking is proposed for the couple crystal mass/concentration, whereas a second way consists in tracking of crystal content and concentration. The controlled variables, unavailable online, are issued from an observer developed in previous works. The performance of these strategies, which application to cane sugar crystallization constitutes a real novelty, are compared with experimental data issued from a PID-controlled industrial plant. The results reveal a significant improvement of energy efficiency, leading to an economy of more than 10% of energy.

关键词： Energy Efficiency Improvement Linearizing Control Industrial Control Phase Changing Crystallization

Economic Performance Optimization of a PV-BESS Power Generator: A Case Study La Reunion Island

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Smart Grid and Renewable Energy 2017年第4期8卷 114-128页

作者： Cédric Damour Michel Benne Frédéric Alicalapa brigitte grondin-perez Jean-Pierre Chabriat University of La Reunion Saint Denis France

This paper proposes an economic performance optimization strategy for a PV plant coupled with a battery energy storage system. The case study of La Reunion Island, a non-interconnected zone (NIZ) with a high level of renewable energy sources (RES), is considered. This last decade, to reach the ambitious target of electricity autonomy by 2030 set by the local authorities, local and national plans have been launched to promote renewable energy sources integration that led to a noticeable development of photovoltaic (PV) systems. To avoid a decrease of the grid reliability due to a large integration of intermittent energy sources into a non-interconnected grid, the authorities have introduced new regulatory rules for RES producers. The proposed optimization strategy relies on these new regulatory rules and takes into account the energy market data, the amount of PV production subject to penalties for imbalance, the batteries and the PV technological characteristics together with a PV production forecast model. Due to its high convergence rate to the true global minimum and its perfect suitability to practical engineering optimization problems, the recently developed Modified Cuckoo Search algorithm is used as optimization algorithm. The effectiveness and relevance of the proposed strategy are assessed on experimental data collected on a real PV power plant. An economical analysis demonstrates that the proposed optimization strategy is able to fulfill the new regulatory rules requirements while increasing the economic performance of the system.

关键词： Power System Economic Analysis and Optimization PV Plant Battery Energy Storage System Metaheuristic Optimization Algorithm

Mechanistic Model versus Artificial Neural Network Model of a Single-Cell PEMFC

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Engineering（科研） 2014年第8期6卷 418-426页

作者： brigitte grondin-perez Sébastien Roche Carole Lebreton Michel Benne Cédric Damour Jean-Jacques Amangoua Kadjo University of La Reunion Saint-Denis France

Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work, with a view to reduce complexity without affecting performance, two different modeling approaches of a single-cell PEMFC are investigated. A mechanistic model, describing all internal phenomena in a single-cell, and an artificial neural network (ANN) model are tested. To perform this work, databases are measured on a pilot plant. The identification of the two models involves the optimization of the operating conditions in order to build rich databases. The two different models benefits and drawbacks are pointed out using statistical error criteria. Regarding model-based control approach, the computational time of these models is compared during the validation step.

关键词： Mechanistic Model Artificial Neural Network Model Proton Exchange Membrane Fuel Cell Real-Time Experiment