Integrated large-scale multivariable control and real-time optimization of a power plant

Emoto, Genichi; Tsuda, A.; Takeshita, Toru; Monical, M.T.; Nakagawa, S.; Fujita, Kunihiko

doi:10.1109/cca.1998.721684

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…There have been some studies on improving header-pressure control in its stability and reliability [4][5][6][7], but none of them consider the optimal fuel consumptions during their pressure regulation. Therefore, an improved header-pressure control with the DLD optimal allocating needs to be developed under the objective of the minimal fuel consumption currently.…”

Section: Introductionmentioning

confidence: 99%

Adaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header

Pan¹,

Shen

Luh

2012

Journal of Dynamic Systems, Measurement, and Control

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An adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the TakagiSugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications.

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Section: Introductionmentioning

confidence: 99%