Conference Paper

An optimal reference governor with a neural network combined model for hybrid Fuel-Cell/Gas-Turbine

Electr. Eng. Dept., Pennsylvania State Univ., University Park, PA, USA
DOI: 10.1109/PES.2010.5590197 In proceeding of: Power and Energy Society General Meeting, 2010 IEEE
Source: IEEE Xplore

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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    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.