In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel referenc...In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.展开更多
The design and implementation of a Generalized Predictive Control(GPC)strategy for the superheated steam temperature regulation in a supercritical(SC)coal-fired power plant is presented.A Controlled Auto-Regressive Mo...The design and implementation of a Generalized Predictive Control(GPC)strategy for the superheated steam temperature regulation in a supercritical(SC)coal-fired power plant is presented.A Controlled Auto-Regressive MovingAverage(CARMA)model of the plant is derived from using the experimental data to approximately predict the plant’s future behavior.This model is required by the GPC algorithm to calculate the future control inputs.A new GPC controller is designed and its performance is tested through extensive simulation studies.Compared with the performance of the plant using a conventional PID controller,the steam temperature controlled by the GPC controller is found to be more stable.The stable steam temperature leads to more efficient plant operation and energy saving,as demonstrated by the simulation results.Plant performance improvement is also tested while the plant experiences the load demand changes and disturbances resulting from the malfunctioning of coal mills.展开更多
基金The National Natural Science Foundation of China(No.51306082,51476027)
文摘In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.
基金supported by the EPSRC Grant(EP/G062889/2),Advantage West Midlands and the European Regional Development Agency(Birmingham Science City Energy Efficiency&Demand Reduction project).
文摘The design and implementation of a Generalized Predictive Control(GPC)strategy for the superheated steam temperature regulation in a supercritical(SC)coal-fired power plant is presented.A Controlled Auto-Regressive MovingAverage(CARMA)model of the plant is derived from using the experimental data to approximately predict the plant’s future behavior.This model is required by the GPC algorithm to calculate the future control inputs.A new GPC controller is designed and its performance is tested through extensive simulation studies.Compared with the performance of the plant using a conventional PID controller,the steam temperature controlled by the GPC controller is found to be more stable.The stable steam temperature leads to more efficient plant operation and energy saving,as demonstrated by the simulation results.Plant performance improvement is also tested while the plant experiences the load demand changes and disturbances resulting from the malfunctioning of coal mills.
