Thermal modeling of a solid oxide fuel cell and micro gas turbine hybrid power system based on modified LS-SVM

For a solid oxide fuel cell (SOFC) integrated into a micro gas turbine (MGT) hybrid power system, SOFC operating temperature and turbine inlet temperature are the key parameters, which affect the performance of the hybrid system. Thus, a least squares support vector machine (LS-SVM) identification model based on an improved particle swarm optimization (PSO) algorithm is proposed to describe the nonlinear temperature dynamic properties of the SOFC/MGT hybrid system in this paper. During the process of modeling, an improved PSO algorithm is employed to optimize the parameters of the LS-SVM. In order to obtain the training and prediction data to identify the modified LS-SVM model, a SOFC/MGT physical model is established via Simulink toolbox of MATLAB6.5. Compared to the conventional BP neural network and the standard LS-SVM, the simulation results show that the modified LS-SVM model can efficiently reflect the temperature response of the SOFC/MGT hybrid system.     

Parameter extraction of fuel cells using hybrid interior search algorithm

Precise modelling of fuel cells is very important for understanding their functioning. In this work, an application of hybrid interior search algorithm (HISA) is proposed to extract the parameters of fuel cells for their electromechanical equations based on nonlinear current‐voltage characteristics. Proposed hybridised algorithm has been developed using evolutionary mutation and crossover operators so as to enhance the modelling capability of interior search algorithm (ISA). To assess the modelling performance of HISA, parameter extraction of two types of fuel cell models, namely, proton exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) have been considered. Modelling performance of HISA, assessed using mean squared error between computed and experimental data, is found to be superior to ISA and several other recently reported prominent optimisation methods. Based on the presented intensive simulation investigations, it is concluded that HISA improves the performance of the basic ISA in terms of fitter solutions, robustness, and convergence rate and therefore offers a promising optimisation technique for parameter extraction of fuel cells.     

Power decoupling control of a solid oxide fuel cell and micro gas turbine hybrid power system

Solid Oxide Fuel Cell (SOFC) integrated into Micro Gas Turbine (MGT) is a multivariable nonlinear and strong coupling system. To enable the SOFC and MGT hybrid power system to follow the load profile accurately, this paper proposes a self-tuning PID decoupling controller based on a modified output-input feedback (OIF) Elman neural network model to track the MGT output power and SOFC output power. During the modeling, in order to avoid getting into a local minimum, an improved particle swarm optimization (PSO) algorithm is employed to optimize the weights of the OIF Elman neural network. Using the modified OIF Elman neural network identifier, the SOFC/MGT hybrid system is identified on-line, and the parameters of the PID controller are tuned automatically. Furthermore, the corresponding decoupling control law is achieved by the conventional PID control algorithm. The validity and accuracy of the decoupling controller are tested by simulations in MATLAB environment. The simulation results verify that the proposed control strategy can achieve favorable control performance with regard to various load disturbances.     

Part-load,startup, and shutdown strategies of a solid oxide fuel cell-gas turbine hybrid system

Current work on the performance of a solid oxide fuel cell (SOFC) and gas turbine hybrid system is presented. Each component model developed and applied is mathematically defined. The electrochemical performance of single SOFC with different fuels is tested. Experimental results are used to validate the SOFC mathematical model. Based on the simulation model, a safe operation regime of the hybrid system is accurately plotted first. Three different part-load strategies are introduced and used to analyze the part-load performance of the hybrid system using the safe regime. Another major objective of this paper is to introduce a suitable startup and shutdown strategy for the hybrid system. The sequences for the startup and shutdown are proposed in detail, and the system responses are acquired with the simulation model. Hydrogen is used instead of methane during the startup and shutdown process. Thus, the supply of externally generated steam is not needed for the reforming reaction. The gas turbine is driven by complementary fuel and supplies compressed air to heat up or cool down the SOFC stack operating temperature. The dynamic simulation results show that smooth cooling and heating of the cell stack can be accomplished without external electrical power.     

Parameter optimization for tubular solid oxide fuel cell stack based on the dynamic model and an improved genetic algorithm

The tubular solid oxide fuel cell (SOFC) stack has important parameters that need to be identified and optimized for the control of high performance. In this paper, a simple SOFC electrochemical model which its parameters need to be optimized is introduced to implement stack control for high output power. A dynamic SOFC model is built based on three sub-models to provide a large numbers simulated data and different condition for optimization. Unlike the traditional parameter optimization method--simple genetic algorithm (SGA), an improved genetic algorithm (IGA) is introduced. The proposed method shows more accuracy and validity by comparing the different results using SGA and IGA methods, the simulated data, and experimental data. The models and IGA method are adapted to control processes.     

Optimization of a solid oxide fuel cell and micro gas turbine hybrid system

For a solid oxide fuel cell (SOFC) and micro gas turbine (MGT) hybrid system, optimal control of load changes requires optimal dynamic scheduling of set points for the system's controllers. Thus, this paper proposes an improved iterative particle swarm optimization (PSO) algorithm to optimize the operating parameters under various loads. This method combines the iteration method and the PSO algorithm together, which can execute the discrete PSO iteratively until the control profile would converge to an optimal one. In MATLAB environment, the simulation results show that the SOFC/MGT hybrid model with the optimized parameters can effectively track the output power with high efficiency. Hence, the improved iterative PSO algorithm can be helpful for system analysis, optimization design, and real‐time control of the SOFC/MGT hybrid system. Copyright © 2011 John Wiley & Sons, Ltd.     

Predictive control of SOFC based on a GA-RBF neural network model

Transients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) system. One of the main reasons is that the fuel utilization changes drastically due to the load change. Therefore, in order to guarantee the fuel utilization to operate within a safe range, a nonlinear model predictive control (MPC) method is proposed to control the stack terminal voltage as a proper constant in this paper. The nonlinear predictive controller is based on an improved radial basis function (RBF) neural network identification model. During the process of modeling, the genetic algorithm (GA) is used to optimize the parameters of RBF neural networks. And then a nonlinear predictive control algorithm is applied to track the voltage of the SOFC. Compared with the constant fuel utilization control method, the simulation results show that the nonlinear predictive control algorithm based on the GA-RBF model performs much better.     

Performance analysis for the part-load operation of a solid oxide fuel cell–micro gas turbine hybrid system

In this paper, the performance evaluation of a solid oxide fuel cell (SOFC)–micro gas turbine (MGT) hybrid power generation system under the part-load operation was studied numerically. The present analysis code includes distributed parameters model of the cell stack module. The conversions of chemical species for electrochemical process and fuel reformation process are considered. Besides the temperature distributions of the working fluids and each solid part of cell module by accounting heat generation and heat transfers, are taken into calculation. Including all of them, comprehensive energy balance in the cell stack module is calculated. The variable MGT rotational speed operation scheme is adopted for the part-load operation. It will be made evident that the power generation efficiency of the hybrid system decreases together with the power output. The major reason for the performance degradation is the operating temperature reduction in the SOFC module, which is caused by decreasing the fuel supply and the heat generation in the cells. This reduction is also connected to the air flow rate supplement. The variable MGT rotational speed control requires flexible air flow regulations to maintain the SOFC operating temperature. It will lead to high efficient operation of the hybrid system.     

Optimal dual-model controller of solid oxide fuel cell output voltage using imitation distributed deep reinforcement learning

Solid oxide fuel cell (SOFC) is disadvantaged by significant nonlinearity, which makes it difficult to control output voltage of SOFC and satisfy the constraints of fuel utilization simultaneously. In order to solve this problem, a dual-model control framework (DMCF) is proposed. In particular, there are two controllers deployed under this framework, with an PID controller and a supplementary dynamic controller to track the SOFC output voltage. The supplementary dynamic controller is conducive to the stabilization of tracking by adapting to the uncertainties, considering the constraint on fuel utilization. In addition, an imitation distributed deep deterministic policy gradient (ID3PG) algorithm, which integrates imitation learning and distributed deep reinforcement learning to enhance the robustness and adaptive capacity of this framework, is proposed for the supplementary dynamic controller. The simulation results obtained in this work have demonstrated that the proposed framework is effective in imposing control on SOFC output voltage and preventing constraint violations of fuel utilization.     

Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation

This study applies adaptive neuro-fuzzy inference system (ANFIS) techniques and artificial neural network (ANN) to predict solid oxide fuel cell (SOFC) performance while supplying both heat and power to a residence. A microgeneration 5 kW_el SOFC system was installed at the Canadian Centre for Housing Technology (CCHT), integrated with existing mechanical systems and connected in parallel to the grid. SOFC performance data were collected during the winter heating season and used for training of both ANN and ANFIS models. The ANN model was built on back propagation algorithm as for ANFIS model a combination of least squares method and back propagation gradient decent method were developed and applied. Both models were trained with experimental data and used to predict selective SOFC performance parameters such as fuel cell stack current, stack voltage, etc.     

Development of solid oxide fuel cell and battery hybrid power generation system

Solid oxide fuel cell hybrid generation system is the best scheme for the load tracking of off-grid monitoring stations. But there are still potential problems that need to be addressed: preventing fuel starvation and ensuring thermal safety while meeting load tracking in hybrid power generation system. In order to solve these problems, a feasible hybrid power generation system structure scheme is proposed which combined SOFC subsystem and Li-ion battery subsystem. Then a model of the hybrid power generation system is built based on the proposed system structure. On this basis, an adaptive controller, include the adaptive energy management algorithm and current feedforward gas supply strategy, is applied to manage the power-sharing in this hybrid system as well as keep the system operating within the safety constraints. The constraints, including maintaining the bus voltage at the desired level, keeping SOFC operating temperature in safety, and mitigating fuel starvation are explicitly considered. The stability of the proposed energy management algorithm is analyzed. Finally, the developed control algorithm is applied to the hybrid power generation system model, the operation result proves the feasibility of the designed controller strategy for hybrid generation system and effectively prevent fuel starvation and ensure thermal safety.     

Fuzzy neural control of a hybrid fuel cell/battery distributed power generation system

An innovative control strategy is proposed of hybrid distributed generation (HDG) systems, including solid oxide fuel cell (SOFC) as the main energy source and battery energy storage as the auxiliary power source. The overall configuration of the HDG system is given, and dynamic models for the SOFC power plant, battery bank and its power electronic interfacing are briefly described, and controller design methodologies for the power conditioning units and fuel cell to control the power flow from the hybrid power plant to the utility grid are presented. To distribute the power between power sources, the fuzzy switching controller has been developed. Then, a Lyapunov based-neuro fuzzy algorithm is presented for designing the controllers of fuel cell power plant, DC/DC and DC/AC converters; to regulate the input fuel flow and meet a desirable output power demand. Simulation results are given to show the overall system performance including load-following and power management of the system.     

A hybrid prognostic model applied to SOFC prognostics

Poor durability is one of the major hurdles for Solid Oxide Fuel Cell (SOFC) commercialization. Prognostic technology is an important approach to improve system durability. However, the current prognostic methods for fuel cells are mainly applied to proton exchange membrane fuel cell (PEMFC) systems, moreover, there are still shortages of recent prognostic algorithms. Therefore, an improved prognostic model is developed to predict the remaining useful life of the SOFC in this study, which combines a hidden semi-Mark model (HSMM) with an empirical model. To build the hybrid prognostic model, an HSMM and an empirical model are firstly built. The merit and demerit of the respective prognostic methodology are then analyzed. According to the analysis results, the hybrid prognostic model is proposed and applied to six sets of SOFC run-to-failure data. The results show the proposed prognostic model has a higher prediction accuracy and a faster forecasting speed compared with the existed approaches for SOFC prognostics.     

Modeling a SOFC stack based on GA-RBF neural networks identification

In this paper, a nonlinear offline model of the solid oxide fuel cell (SOFC) is built by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of modeling, the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. Furthermore, we utilize the gradient descent learning algorithm to adjust the parameters. The validity and accuracy of modeling are tested by simulations. Besides, compared with the BP neural network approach, the simulation results show that the GA-RBF approach is superior to the conventional BP neural network in predicting the stack voltage with different temperature. So it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA.     

Performance comparison of cocurrent and countercurrent flow solid oxide fuel cells

Solid oxide fuel cell (SOFC) is a complicated system with heat and mass transfer as well as electrochemical reactions. The flowing configuration of fuel and oxidants in the fuel cell will greatly affect the performance of the fuel cell stack. Based on the developed mathematical model of direct internal reforming SOFC, this paper established a distributed parameters simulation model for cocurrent and countercurrent types of SOFC based on the volume-resistance characteristic modeling method. The steady-state distribution characteristics and dynamic performances were compared and were analyzed for cocurrent and countercurrent types of SOFCs. The results indicate that the cocurrent configuration of SOFC is more suitable with regard to performance and safety.     

Nonlinear dynamic modeling for a SOFC stack by using a Hammerstein model

Solid oxide fuel cell (SOFC) is a kind of nonlinear, multi-input–multi-output (MIMO) system that is hard to model by the traditional methodologies. For the purpose of dynamic simulation and control, this paper reports a dynamic modeling study of SOFC stack using a Hammerstein model. The static nonlinear part of the Hammerstein model is modeled by a radial basis function neural network (RBFNN), and the linear part is modeled by an autoregressive with exogenous input (ARX) model. To estimate the hidden centers, the radial basis function widths and the connection weights of the RBFNN, a new gradient descent algorithm is derived in the study. On the other hand, the least squares (LS) algorithm and Akaike Information Criteria (AIC) are used to estimate the parameters and the orders of the ARX model, respectively. The applicability of the proposed Hammerstein model in modeling the nonlinear dynamic properties of the SOFC is illustrated by the simulation. At the same time, the experimental comparisons between the Hammerstein model and the RBFNN model are provided which show a substantially better performance for the Hammerstein model. Furthermore, based on this Hammerstein model, some control schemes such as predictive control, robust control can be developed.     

Novel and optimal integration of SOFC-ICGT hybrid cycle: Energy analysis and entropy generation minimization

Integrating fuel cells with conventional gas turbine based power plant yields higher efficiency, especially solid oxide fuel cell (SOFC) with gas turbine (GT). SOFCs are energy efficient devices, performance of which are not limited to Carnot efficiency and considered as most promising candidate for thermal integration with Brayton cycle. In this paper, a novel and optimal thermal integration of SOFC with intercooled-recuperated gas turbine has been presented. A thermodynamic model of a proposed hybrid cycle has been detailed along with a novelty of adoption of blade cooled gas turbine model. On the basis of 1st and 2nd law of thermodynamics, parametric analysis has been carried out, in which impact of turbine inlet temperature and compression ratio has been observed on various output parameters such as hybrid efficiency, hybrid plant specific work, mass of blade coolant requirement and entropy generation rate. For optimizing the system performance, entropy minimization has been carried out, for which a constraint based algorithm has been developed. The result shows that entropy generation of a proposed hybrid cycle first increases and then decreases, as the turbine inlet temperature of the cycle increases. Furthermore, a unique performance map has also been plotted for proposed hybrid cycle, which can be utilized by power plant designer. An optimal efficiency of 74.13% can be achieved at TIT of 1800 K and r_p,c 20.     

Performance characteristics of part-load operations of a solid oxide fuel cell/gas turbine hybrid system using air-bypass valves

In spite of the high-performance characteristics of a solid oxide fuel cell/gas turbine (SOFC/GT) hybrid system, it is difficult to maintain high-level performance under real application conditions, which generally require part-load operations. The efficiency loss of the SOFC/GT hybrid system under such conditions is closely related to that of the gas turbine. The power generated by the gas turbine in a hybrid system is much less than that generated by the SOFC, but its contribution to the efficiency of the system is important, especially under part-load conditions. Over the entire operating load profile of a hybrid system, the efficiency of the hybrid system can be maximized by increasing the contribution of power coming from the high efficiency component, namely the fuel cell. In this study, part-load control strategies using air-bypass valves are proposed, and their impact on the performance of an SOFC/GT hybrid system is discussed. It is found that air-bypass modes with control of the fuel supply help to overcome the limits of the part-load operation characteristics in air/fuel control modes, such as variable rotational speed control and variable inlet guide vane control.     

天然气重整SOFC/MGT混合分布式供能系统性能分析

针对固体氧化物燃料电池(SOFC)与微型燃气轮机(MGT)构成的混合分布式供能系统,首先建立了一种管式SOFC准二维数值模型,优化了辐射计算,提高了热传递模型的准确性;考虑了CO及H2同时作为燃料参加电化学反应,并完善了损失计算模型;最后采用所发展的系统性能预测模型,分别在内部重整和外部重整情况下,预测比较了两种SOFC/MGT混合系统的性能,结果表明外部重整系统在系统输出功率、CO2排放以及热应力分布方面都比内部重整系统具有优势,然而这种轻微的优势是需要额外增加外部重整器的设备投资换取的。