Coordinated control system modeling of ultra-supercritical unit based on a new fuzzy neural network

The coordinated control systems (CCS) in ultra-supercritical thermal power unit, like many other in-dustrial systems, is a complex multivariable system with severe nonlinearity, strong multivariable coupling and uncertainties. In order to meet the requirements of operational stability, economy. etc in ultra-supercritical unit, it is necessary to establish its accurate mathematical model and further design the advanced controller. Against this background, a new fuzzy neural network modeling method is proposed in this paper. First of all, the incremental model is considered separately to improve the ra-tionality of the local linear model structure. Then, the parameters in antecedent part is initialized by a kernel k-means++ algorithm, in which Xie-Beni index is used to optimize the number of fuzzy rules. Finally, supervised adaptive gradient descent algorithm and artificial immune particle swarm optimi-zation algorithm work in stages to complete the training of the consequent part parameters. The pro-posed modeling method in this paper is applied to a 1000 MW unit in China and shows satisfactory accuracy. In the established model, the MSE of power output, main steam pressure and separator outlet steam temperature are 0.0099, 1.21E-4, 0.0023, respectively. Both numerical and graphical simulation results confirm the effectiveness of the presented fuzzy neural network in modeling. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A new fuzzy neural network modeling method is proposed in this study to accurately model the ultra-supercritical thermal power unit, meeting the requirements of operational stability and economy. The method shows satisfactory accuracy when applied to a 1000 MW unit in China, with numerical and graphical simulation results confirming its effectiveness.