Research On Multi-model Predictive Hierarchical Scheduling Control In Coordination Control System Of Ultra-supercritical Unit

Coordination system of large-capacity thermal power units has characteristics of strong nonlinearity and large delay.Traditional control algorithm is not ideal when the unit operates in a large range of conditions.An accurate model is necessary for designing advanced controllers.In this paper,based on an ultra-supercritical unit coordination system,modeling and control simulation are studied.The main research results include the following aspects:1.Based on particle swarm optimization algorithm,the variation strategy of inertia weight and learning factor are improved.A boundary division variation particle swarm optimization(BDVPSO)algorithm is proposed.Tested with different functions,the results show that the proposed algorithm converges in a fast speed,and seldom falls into local optimum in dealing with complex multi-dimensional optimization problems.According to the relevant characteristics of the unit operation,based on the law of mass,energy and momentum conservation,the nonlinear model structure of the coordination system is established.According to the step response test data at different load points of the unit,the proposed optimization algorithm is used to optimize the model structure parameters.Verified with field data,the results show that the established nonlinear model has high precision and can describe the dynamic characteristics of the unit operating in large working conditions.2.Based on the gap metric theory,the nonlinear analysis of the established coordination system is carried out.By establishing a local linearization model at each load point,the gap metric between each linearized system is calculated,and the nonlinearity of the system is analyzed according to the characteristics of the gap metric.The results show that the degree of nonlinearity of the coordination system decreases with the increase of the load level.In order to describe the system operating characteristics of the same operating range,the number of submodels required for the low-load section is higher than that of the high-load section.The load points of the system need to be re-determined according to the principle of equal distance.The number of sub-models is determined according to the gap metric threshold.According to the sub-model at each load point,the gap metric curve of the load point is fitted according to the gap metric between the sub-system and the adjacent working conditions,which is beneficial to intuitively and quantitatively analyze the nonlinearity of the system within the normal operating conditions of the unit.3.Because of the characteristics of large inertia and nonlinearity of the ultra-supercritical unit coordination system,a multi-model predictive control algorithm based on hierarchical scheduling is proposed.Based on the multivariable generalized predictive control algorithm,a controller weighting strategy is proposed using the gap metric curve function to improve the stability of controllers.According to the hierarchical scheduling idea,the multi-layer model structure is designed according to the distance between different models.The model accuracy and coverage conditions are different in different levels,and it is suitable for the control under different operating conditions.Simulated on the established coordination system by 2% MCR and 3% MCR load change rate,and compared with the traditional multi-model predictive control algorithm,the results show that the proposed control algorithm has advantages on accuracy,rapidity and applicability.