An optimal reference governor with a neural network combined model for hybrid Fuel-Cell/Gas-Turbine
ABSTRACT This paper introduces a concept of real-time optimization of hybrid fuel-cell power plants as an alternative distributed generation source that improves the power quality and reliability of the power grid. One of the most important issues of plant operation is the optimal control of the power plant, leading to significant economic and environmental benefits. As a commercialized fuel cell technology, Direct Fuel-Cell with Gas-Turbine (DFC/T) power plant is investigated in this paper. A framework of an optimal reference governor (ORG) is developed to generate optimal control strategies for the local controllers. For the purpose of on-line application, a neural network combined model is built as a state estimator that approximates the plant behaviors, which is compatible with population based real-time heuristic optimization algorithms. The simulation of the optimization result is presented and validated by a comparison with experimental data and simulation result of a mathematical plant model.
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- "There are many kinds of fuel cell power plants. The Morton Carbonate Fuel Cell (MCFC) has high efficiency and high power density and thus considered to be the most promising fuel-cell power plant for large-scale power generation . The fuel-cell power plant mathematical model used in this paper was first developed by Lukas, Lee and Ghezel-Ayagh . "
ABSTRACT: Power conditioning system is an interface between distributed generation and utility grid. This paper presents the model of a power conditioning system (PCS) for the hybrid direct fuel-cell/turbine (DFC/T) power plant. It regulates voltage, current and power transmitted from the hybrid DFC/T power plant to utility grid. The proposed system consists of DC/DC converters, a grid-connected SPWM DC/AC inverter, and an LCL filter. To regulate and stabilize the DC link voltage, DC/DC converter with PI controller is adopted. With the dual-loop PI based grid-connected SPWM inverter, both active and reactive power transmitted to utility grid from the hybrid DFC/T power plant can be controlled. The LCL filter can greatly reduce the current harmonic distortion (THD). Theoretical analysis, modeling methodologies and control schemes are presented. The whole model was developed in Matlab/Simulink environment with all the parameters given. Simulation results demonstrate that the proposed PCS can follow a dynamic load with the error less than 1% and reduce the THD to 1.65%.01/2011; DOI:10.1109/CDC.2011.6160741
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ABSTRACT: Power conditioning system (PCS) is an interface between distributed generation and utility grid. It regulates voltage, current and power transmitted from the hybrid direct fuel-cell/turbine (DFC/T) power plant to utility grid. This paper presents a self-adaptive fuzzy PI controller of three phase inverter in PCS for the DFC/T power plant. One of the main tasks of PCS for distributed generations is power flow control. Traditionally, the control scheme for grid connected inverters is PI controller. However, PI control scheme would lead to large overshoot and long response time when the load increases sharply. Thus, a hybrid fuzzy PI control scheme is proposed in this paper in order to improve the power flow control. The fuzzy controllers can adjust the PI parameters according to the voltage error and the derivative of voltage error. The overall self-adaptive tuning fuzzy PI controller can update the PI parameters in real time, which makes the power flow control much more accurate than conventional PI controllers. Analysis and design methodologies of hybrid fuzzy PI controllers are presented in this paper. The model was developed in Matlab/Simulink environment. Simulation results show that the proposed control scheme can effectively reduce the overshoot and response time compared to the conventional PI controllers.
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ABSTRACT: Development of Smart Grid requires power plants to be more intelligent, efficient, and reliable, which raises new challenges of the control system design for modern power plants. Regarding these requirements, an integrated multi-task control system using artificial intelligence technologies is proposed to improve the efficiency and reliability of a hybrid fuel-cell with gas turbine power plant. The integrated control system consists of a hybrid Neural Network plant model with online learning ability, an Optimal Reference Governor generating optimal setpoints as local control references, and a Fault Diagnosis and Accommodation system to detect internal plant faults and to regulate the plant during plant failures. The three subsystems are integrated to provide compressive management for the power plant. The hybrid fuel-cell power plant is introduced; the structure and strategies of the control system are discussed, and simulation results are presented.01/2011; DOI:10.1109/CDC.2011.6160742