Advanced control algorithms for steam temperature regulation of thermal power plants

Sanchez-Lopez, Alfredo; Figueroa, Gustavo; Villavicencio-Ramirez, A.

doi:10.1016/j.ijepes.2004.08.003

Cited by 46 publications

(14 citation statements)

References 8 publications

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“…In this regard, artificial neural networks (ANN), fuzzy logic (FL) models or a combination of these approaches such as adaptive neuro-fuzzy inference systems (ANFIS) are extensively used for modeling the industrial processes including power plants. This [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 93%

Neuro-Fuzzy Modeling of Heat Recovery Steam Generator

Ghaffari¹,

Chaibakhsh²,

Shahhoseini³

2012

IJMLC

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Abstract-In this paper, an application of dynamic neuro-fuzzy systems is presented for modeling the subsystems of the heat recovery steam generator (HRSG). The dynamic neuro-fuzzy models were developed based on the formal NARX models topology. The clustering techniques were employed to define the structure of the fuzzy models by dividing the entire operating regions into smaller subspaces. The optimal cluster centers and corresponding membership functions are captured by FCM, where the parameters of consequent were adjusted by recursive LSE method. A comparison between the responses of the proposed models and the responses of the plants ware preformed, which validates the accuracy and performance of the modeling approach.

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Section: Introductionmentioning

confidence: 93%

Neuro-Fuzzy Modeling of Heat Recovery Steam Generator

Ghaffari¹,

Chaibakhsh²,

Shahhoseini³

2012

IJMLC

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“…It shows that the step‐response model based on the test data is better suited than the linearized model. In Refs and , DMC techniques have been applied to control the superheater/reheater steam temperature of the FFPPs, the results demonstrate that better control performance can be achieved as compared to the conventional PID control.…”

Section: Advanced Control Of the Ffppmentioning

confidence: 99%

Steam power plant configuration, design, and control

Shen

et al. 2015

WIREs Energy & Environment

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This article provides an overview of fossil‐fuel power plant (FFPP) configuration, design and especially, the control technology, both the conventional and the advanced technologies. First, a brief introduction of FFPP fundamentals and configurations are presented, followed by the description of conventional PID‐based control system in the FFPPs and its short‐comings. As the major part of this writing, different advanced control strategies and applications are reported, with their significant features outlined and discussed. These new technologies are collected from both the academic studies and industrial practices, which can improve the performance of the FFPP control system for more economic and safe plant operation. The final section presents a view of the next generation FFPP control technologies, emphasizing potential business and research opportunities. WIREs Energy Environ 2015, 4:537–563. doi: 10.1002/wene.161 This article is categorized under: Fossil Fuels > Systems and Infrastructure Energy Efficiency > Systems and Infrastructure Energy Infrastructure > Science and Materials Energy Infrastructure > Systems and Infrastructure

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“…Model-based controllers have also been proposed, such as the constrained generalized predictive controller [9], the multi-step predictive controller with an exponential-ARX model [10], dynamic matrix control [11], and the internal model control based on a least mean squares adaptive filter [12]. The performances of the model-based controllers were found not to be satisfactory without the explicit implementation of flue gas behavior predictors, which are complicated to construct.…”

Section: Introductionmentioning

confidence: 99%

Prediction-based feedforward control of superheated steam temperature of a power plant

Kim

et al. 2015

International Journal of Electrical Power & Energy Systems

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Advanced control algorithms for steam temperature regulation of thermal power plants

Cited by 46 publications

References 8 publications

Neuro-Fuzzy Modeling of Heat Recovery Steam Generator

Neuro-Fuzzy Modeling of Heat Recovery Steam Generator

Steam power plant configuration, design, and control

Prediction-based feedforward control of superheated steam temperature of a power plant

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