Abstract
A suitable model of coordinated control system (CCS) with high accuracy and simple structure is essential for the design of advanced controllers which can improve the efficiency of the ultra-super-critical (USC) power plant. Therefore, with the demand of plant performance improvement, an improved T-S fuzzy model identification approach is proposed in this paper. Firstly, the improved entropy cluster algorithm is applied to identify the premise parameters which can automatically determine the cluster numbers and initial cluster centers by introducing the concept of a decision-making constant and threshold. Then, the learning algorithm is used to modify the initial cluster center and a new structure of concluding part is discussed, the incremental data around the cluster center is used to identify the local linear model through a weighted recursive least-square algorithm. Finally, the proposed approach is employed to model the CCS of a 1000 MW USC one-through boiler power plant by using on-site measured data. Simulation results show that the T-S fuzzy model built in this paper is accurate enough to reflect the dynamic performance of CCS and can be treated as a foundation model for the overall optimizing control of the USC power plant.
Highlights
Ultra-super-critical (USC) power plants have been widely developed during the past decade for their high efficiency and low emission
For a large-scale once-through boiler power plant, the boiler and turbine are controlled as a whole object and the coordinated control system (CCS) is adopted
In order to improve the performance of a USC power plant, it is essential to build a model of CCS with high accuracy and simple structure
Summary
Ultra-super-critical (USC) power plants have been widely developed during the past decade for their high efficiency and low emission. In order to improve the performance of a USC power plant, it is essential to build a model of CCS with high accuracy and simple structure. This modeling problem has been addressed in few reports, and the appropriate identification methods still need to be deeply researched. In [15], an entropy-based fuzzy clustering method was proposed for reducing the computation load In this algorithm, the entropy just needs to be calculated once in the searching process, and the data pairs which have minimum entropy will be selected as initial cluster centers.
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