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.
Conference Paper: A robust feasibility problem for the design of a reference governor[Show abstract] [Hide abstract]
ABSTRACT: We formulate a robust feasibility problem for the design of a reference governor to provide setpoints for the lower-level control in a two-layer hierarchical system. Using linear programming duality, solutions to the robust feasibility problem (i.e. both necessary and sufficient conditions for the existence of an admissible reference) are given. Three cases are considered: 1) Fixed reference; 2) feedforward management; and 3) affine feedback management. The computationally efficient results can be implemented in supervisory control in SCADA networks.American Control Conference (ACC), 2012; 06/2012
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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: 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