Multi-model predictive control for coordination system of double-reheat power unit

doi:10.16265/j.cnki.issn1003-3033.2022.S2.0034

Abstract

Abstract:

In order to improve the operational flexibility and safety stability of the double-reheat coal-fired power unit, a new turbine-boiler coordinated control system was designed by combining the MPC algorithm and the multi-model method. Based on the idea of grey-box modeling, a nonlinear mechanism model that can accurately reflect the dynamic characteristics of the turbine-boiler coordination system of the double-reheat coal-fired power unit was established. The mechanism model of the turbine-boiler coordination system was analyzed by using Gap metric theory, the dynamic characteristics difference between local linear models is calculated quantitatively, the model set was constructed and the weighting rules were formulated. Based on the pre-established weighting rules, the corresponding MPC local controller was designed for each local linear model, and then integrated into the global control effect. In order to quantitatively characterize the operation safety level of coal fired power generation units, the hourly mean values of the main steam pressure at the turbine side and the steam temperature at the outlet of the steam water separator were established. The engineering application shows that the multi-model predictive control of the turbine-boiler coordinated system improves the performance of the coordinated control system, and can maintain the stability of the main parameters and broaden the safe operation boundary while ensuring the rapid response to the power grid's load demand.

Key words: double-reheat, coordination system, multi-model, model predictive control (MPC), Gap metric

ZHOU Yang. Multi-model predictive control for coordination system of double-reheat power unit[J]. China Safety Science Journal, 2022, 32(S2): 81-87.

Figures/Tables 7

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模型	P₁	P₂	P₃	P₄	P₅	P₆	P₇
P₁	0	0.231 6	0.373 4	0.470 1	0.505 5	0.536 0	0.554 8
P₂	0.231 6	0	0.155 6	0.268 1	0.309 4	0.345 8	0.368 5
P₃	0.373 4	0.155 6	0	0.118 3	0.161 6	0.223 7	0.267 0
P₄	0.470 1	0.268 1	0.118 3	0	0.080 8	0.151 7	0.196 0
P₅	0.505 5	0.309 4	0.161 6	0.080 8	0	0.072 9	0.121 2
P₆	0.536 0	0.345 8	0.223 7	0.151 7	0.072 9	0	0.051 2
P₇	0.554 8	0.368 5	0.267 0	0.196 0	0.121 2	0.051 2	0