Fuzzy K-Means Cluster Based Generalized Predictive Control of Ultra Supercritical Power Plant

This article proposes a fuzzy k-means cluster based generalized predictive control (GPC) method for a 1000 MW ultra supercritical power plant to improve of the boiler combustion efficiency. First, to fully use the statistic characteristic of the historical data, a fuzzy k-mean cluster network (FKN) is well constructed to derive the local linear models, and the nonlinear dynamic process of studied system is elaborately approximated by the fuzzy combination of the local linear models. Then, a global GPC method is proposed to improve the control performance by using the membership of the current FKN. Different from the traditional GPC, the advantage of proposed GPC is that local GPC is fuzzily combined together to achieve the purpose of global GPC by a scheduling algorithm. Finally, an example illustrates that the proposed control strategy can achieve the satisfactory performance.

Automated summary

This article proposes a fuzzy k-means cluster based generalized predictive control method for improving boiler combustion efficiency in a 1000 MW ultra supercritical power plant. By constructing a fuzzy k-means cluster network, the nonlinear dynamic process of the system is approximated and a global GPC method is introduced. An example demonstrates the satisfactory performance of the proposed control strategy.