Intelligent modified predictive optimal control of reheater steam temperature in a large-scale boiler unit

Lee, Kwang Y.; Ma, Liangyu; Boo, Chang-Jin; Jung, Won-Hee; Kim, Sung Hoon

doi:10.1109/pes.2009.5275381

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…Simulation results show that a satisfactory control of main steam pressure and temperature and reheat steam temperature can be attained during load‐cycling and other severe plant operating conditions. To improve the computational performance of the nonlinear optimization, the particle swarm optimization (PSO) and its modifications have been used in Refs to search for the optimal control sequence in the NN based nonlinear MPC; the advantages and effectiveness of these approaches have been clearly shown through simulations of the superheater and reheater steam temperature control of the FFPPs. In Refs and online‐update diagonal recurrent neural network (DRNN) models have been developed for 500 and 1000 MW FFPPs, and PSO based nonlinear MPCs are then designed to achieve a plant‐wide control.…”

Section: Advanced Control Of the Ffppmentioning

confidence: 99%

“…To provide dynamic information about the plant for controller design, the usual NN modeling is combining the conventional feedforward networks (such as BP or RBF neural networks) with tapped delays . Beside this, in Refs , Elman neural networks (ENN) and their modifications are utilized to approximate the behavior of the steam temperature systems of FFPPs. The ENN differs from the conventional feedforward networks in that it includes recurrent or feedback connections.…”

Section: Advanced Control Of the Ffppmentioning

confidence: 99%

“…With the dynamic process model being successfully developed, various advanced controller can be designed, for example, the aforementioned Auto‐tuning PI/PID Control and nonlinear MPC. To generate solutions to the highly complex nonlinear optimization problem, heuristic optimization techniques, such as evolutionary programming (EP), genetic algorithm (GA) or particle swarm optimization (PSO) have been employed extensively, providing quality solutions and fast convergence compared with the conventional nonlinear programming.…”

Section: Advanced Control Of the Ffppmentioning

confidence: 99%

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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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Section: Advanced Control Of the Ffppmentioning

confidence: 99%

Section: Advanced Control Of the Ffppmentioning

confidence: 99%

Section: Advanced Control Of the Ffppmentioning

confidence: 99%

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Steam power plant configuration, design, and control

Shen

et al. 2015

WIREs Energy & Environment

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“…It can increase thermal efficiency by 2% and it can also reduce steam humidity and improve the safety of the final stage's blade [1,2]. However, due to the complexity of the many influential factors, it is difficult to maintain the reheat steam temperature within a certain range [3]. For instance, the reheater steam temperature of two ultra-super-critical 1000 MW units investigated in this paper may fluctuate between 565 • C and 610 • C, while the normal reheater outlet steam temperature is 603 • C with tolerable fluctuation within the range of 503 to 608 • C [4] (the specific threshold may vary with the type of reheater).…”

Section: Introductionmentioning

confidence: 99%

Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction

et al. 2019

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Accurately predicting the reheater steam temperature over both short and medium time periods is crucial for the efficiency and safety of operations. With regard to the diverse temporal effects of influential factors, the accurate identification of delay orders allows effective temperature predictions for the reheater system. In this paper, a deep neural network (DNN) and a genetic algorithm (GA)-based optimal multi-step temporal feature selection model for reheater temperature is proposed. In the proposed model, DNN is used to establish a steam temperature predictor for future time steps, and GA is used to find the optimal delay orders, while fully considering the balance between modeling accuracy and computational complexity. The experimental results for two ultra-super-critical 1000 MW power plants show that the optimal delay orders calculated using this method achieve high forecasting accuracy and low computational overhead. Moreover, it is argued that the similarities of the two reheater experiments reflect the common physical properties of different reheaters, so the proposed algorithms could be generalized to guide temporal feature selection for other reheaters.

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“…Many research on neural networks for power station steam temperature control have also been made [7][8][9][10]. Adaptive inverse control method is with clear physical concept, and is intuitive and easy to understand.…”

Section: Introductionmentioning

confidence: 99%

Neural network based superheater steam temperature control for a large-scale supercritical boiler unit

Lee

2011

2011 IEEE Power and Energy Society General Meeting

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To improve the Superheater Steam Temperature (SST) control of a large-scale supercritical coal-fired boiler generating unit, this paper presents an inverse compensation control scheme based on dynamic recurrent neural network (NN) inverse process models for the multi-stage water-spray desuperheating controllers. With proper analysis of the boiler design and operational characteristics, the inputs and outputs of the NN inverse models are determined. Then two NN inverse dynamic models of the superheater system are trained and validated with historical operating data over a wide-range of loading conditions. The trained NN inverse models are then employed as internal model controllers to improve the SST control effect by providing real-time supplementary signals to the original cascade PID controllers. The real-time steam temperature signals are fed back to adjust the input reference values of the NN controllers automatically. The controller is programmed in MATLAB and communicates with a full-scope simulator of a 600MW supercritical coal-fired power generating unit. Detailed simulation tests are carried out, which shows the new compensation control scheme can dramatically improve the SST control of the supercritical boiler. 1 Index Terms-Dynamic recurrent neural network, inverse model, supercritical boiler, superheater steam temperature control, compensation control. L. Y. Ma is with the

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Intelligent modified predictive optimal control of reheater steam temperature in a large-scale boiler unit

Cited by 13 publications

References 18 publications

Steam power plant configuration, design, and control

Steam power plant configuration, design, and control

Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction

Neural network based superheater steam temperature control for a large-scale supercritical boiler unit

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