| With the sustainable development of electric power industry, as the basic unit of power grid, coal fire power unit undertakes the task of meeting the basic requirements of power grid load, peak shaving, and frequency modulation. Coal fire power unit plays an important role in the whole safe, stable and economic operation process. In this paper, taking coal fire power unit as a research object, related research works mainly focus on state observer design and control algorithm application. Then combined state observer with advanced control algorithm, the better control quality of unit can be achieved.Firstly,660MW subcritical unit and 1000MW ultra supercritical unit are analyzed on dynamic characteristics to illustrate coal fire power unit with strong coupling and nonlinear control difficulty. Secondly, based on these difficulties of coal fire power unit control, two kinds of state observer design methods of nonlinear systemd are introduced. One is classical Luenberger state observer method, the other is improved Luenberger state observer design method based on the characteristic of BP neural network which can approximate nonlinear function. Thirdly, these two state observers are applied to 660MW subcritical unit and 1000MW ultra supercritical unit. Simulation results demonstrate that the two state observers can track states and outputs of system. Comparison results show that the improved state observer can estimate more fast and accurately.Finally, since coal fire power unit is a nonlinear system with the characteristics of strong coupling, large inertia, slow time-varying parameters, model predictive control algorithm based on state observer is adopted. According to states and outputs in the past time, model predictive control algorithm can predict system future outputs to achieve stable and rapid control performance. Simulation results show that model predictive control algorithm based on state observer can obtain better control quality in coal fire power unit. |
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