Abstract
This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.
Highlights
During the past 30 years, the technology of renewable energy power generation has been developed rapidly in China
The local model network (LMN) model of the 500 MW coal-fired B-T unit shown in Figure 1 is identified to provide the prediction mode for the proposed nonlinear predictive control method
The performance of linear model-based MPC (LMPC) is worse than the nonlinear model predictive control (NMPC), especially in the main steam pressure, where a huge control offset occurred under low-load conditions, which is due to the significant modeling mismatch caused by the nonlinearity of the B-T unit
Summary
During the past 30 years, the technology of renewable energy power generation has been developed rapidly in China. Based on the fuzzy model, an offset-free MPC using a genetic algorithm [16] and a nonlinear model predictive iterative learning control [17] were presented for a 160 MW drum-type B-T unit, which were shown to be effective in a wide-range operation. The idea of improving robustness of energy systems is further explored by active disturbance rejection control (ADRC) approaches [27,28], such as nonlinear disturbance rejection control with the combination of a stable feedback controller and a sliding mode observer [29], ADRC based on direct energy balance [30], and robust mode predictive control [31,32] Most of these approaches, except two enhanced MPCs in [31] and [32], cannot effectively deal with the constraints, which may deteriorate the control performance of CCS in practice. PTrohbeleImGAatseovlevreys stahme polpintigmpiezraitoiodntopfironbdlethme oatpteivmearlycosanmtropllisnegqupeenrcioedthtaot cfianndmtheeet othpeticmoanlstcroanintrtsol asnedqumeinncime tihzeatthcaenombjeecettivtheefuconnctsitornain(ptserafnodrmmainnciemiinzdeetxh)e. objective function (performance index)
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