A new T–S fuzzy-modeling approach to identify a boiler–turbine system

Li, Chaoshun; Zhou, Jianzhong; Li, Qingqing; An, Xueli; Xiang, Xiuqiao

doi:10.1016/j.eswa.2009.07.052

Cited by 71 publications

(21 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation (11) is used in [14]. e i is the identification error, and the nonlinear data around the cluster center is used directly to get the local model.…”

Section: Discussion Of the New Structurementioning

confidence: 99%

“…However, the cluster results always depend on the initial values which are specified manually. The cluster number of fuzzy c-regression models (FCRM) [14] is also selected manually. When the cluster number is changed to find the optimal value, the whole cluster process should run again and again, which will lead to heavy computational effort.…”

Section: Introductionmentioning

confidence: 99%

“…After getting the cluster centers and radius of the data, the parameters of conclusion part will be calculated by the parameter estimation method, such as subspace identification (SID) methods [1], orthogonal least square method [14], weighted recursive least-square algorithm [16], and so on. However, the structure of the concluding part was rarely discussed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Approach of Modeling an Ultra-Super-Critical Power Plant for Performance Improvement

Hou

Jiang

et al. 2016

Energies

View full text Add to dashboard Cite

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.

show abstract

“…Equation (11) is used in [14]. e i is the identification error, and the nonlinear data around the cluster center is used directly to get the local model.…”

Section: Discussion Of the New Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Approach of Modeling an Ultra-Super-Critical Power Plant for Performance Improvement

Hou

Jiang

et al. 2016

Energies

View full text Add to dashboard Cite

show abstract

“…We get 2000 data points by simulating the boiler-turbine system, which is described earlier, among which the first 1500 samples are treated as training data, and the last 500 as test data. For the purpose of comparison, we select the fuzzy model input signals that are the same as the works of Li et al [8][9][10] Three different fuzzy models M1, M2, and M3 are built to describe drum pressure, electrical output, and drum water level of the original boiler-turbine system separately. For the ith…”

Section: Boiler-turbine Systemmentioning

confidence: 99%

“…The clustering prototype in a fuzzy space partition is hyperplane; thus, the FCRM clustering technique is presented, which is deduced from the fuzzy clustering objective function of FCRM with the Lagrange multiplier rule, possessing integrative, and concise structure. The same, to build an accurate model for complicated nonlinear system in engineering, like the boiler-turbine system, a novel fuzzy-modeling approach is proposed in the work of Li et al, 9 which is based on a new FCRM clustering algorithm. Li et al proposed and illuminate the gravitational search algorithm-based hyperplane clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy C‐regression model algorithm using a new PSO algorithm

Adel

Soltani

Châari

2017

Adaptive Control & Signal

View full text Add to dashboard Cite

SummaryIn this paper, a new methodology is introduced for the identification of the parameters of the multiple-input-multiple-output local linear Takagi-Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi-Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. KEYWORDSchaos adaptive particle swarm optimization, fuzzy C-regression model clustering algorithm, identification, multiple-input multiple-output, Takagi-Sugeno fuzzy models

show abstract