Optimization control for the coordinated system of an ultra-supercritical unit based on stair-like predictive control algorithm

Zeng, Deliang; Gao, Yaokui; Hu, Yong; Liu, Jizhen

doi:10.1016/j.conengprac.2018.10.001

Cited by 31 publications

(5 citation statements)

References 16 publications

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“…The model described in [10] was used by Zeng et al (2019) [19] to suggest an optimal control strategy. The core of this boiler combustion delay and inertia control technique is a stair-like predictive control algorithm.…”

Section: Control System Reviewmentioning

confidence: 99%

Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer

2022

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Sliding pressure control is a well-known method of controlling supercritical power plants that improves energy efficiency and reduces pressure dynamic stresses. This paper presents a novel approach for developing a supercritical cleaner coal power plant’s sliding pressure control strategy. First, using Whale Optimizer, a nonlinear identified transfer matrix model was created (WO). By comparing simulations and errors, the WO clearly outperforms the GA and Grey-Wolf Optimizer (GWO) techniques on parameter identification. The model also includes a multivariable PI/PD controller for improved plant operation. Again, WO controller tuning outperformed GA and GWO in terms of pressure deviations, power deviations, rise time, and fuel usage. It is now argued that the WO is superior to other techniques in modeling and controlling system dynamics, energy efficiency, and cleaner operation.

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Section: Control System Reviewmentioning

confidence: 99%

Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer

2022

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“…Carbon peaking and carbon neutrality goals can be realized by continuously increasing the installed capacity of renewable energy power in China, but thermal power units need to participate in long-term flexible regulation to curb the impact of renewable energy power on the stable operation of grids (Meysam et al, 2017;Kang et al, 2020;Shahbaz et al, 2020;Zhang et al, 2021;Tan et al, 2022). Conditions change frequently during the flexible operation of thermal power units (Zeng et al, 2019;Wang et al, 2020a), resulting in large changes in the NO x concentration at the entrance of selective catalytic reduction (SCR) denitrification systems. This scenario hinders the effective control of denitrification systems of thermal power units.…”

Section: Introductionmentioning

confidence: 99%

NOx concentration prediction in coal-fired power plant based on CNN-LSTM algorithm

et al. 2023

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Measuring the nitrogen oxides concentration accurately at the inlet of the selective catalytic reduction denitrification system plays an important role in controlling the nitrogen oxides concentration for coal-fired power plants, and a coupling relationship exists between nitrogen oxides concentration and multiple operational variables. Here, a modeling method based on feature fusion and long short-term memory network is proposed to mine the spatial and temporal coupling relationship between input variables for improving the prediction accuracy. First, the collected data were converted to image-like sequences. Then, the high-dimensional features of image-like sequences were fused by a convolutional neural network, and the spatial coupling features among the variables were mined. Finally, the constructed fusion features were input into the long short-term memory network to further explore the time coupling characteristics among the variables and complete the prediction of nitrogen oxides concentration at the inlet of the selective catalytic reduction denitrification system. The simulation results show that the prediction error of nitrogen oxides concentration at the inlet of selective catalytic reduction denitrification system based on CNN-LSTM model is 15.15% lower than that of traditional LSTM model.

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“…Based on a simple two-input and two-output CCS model, a PID-based boiler steam turbine controller was proposed in ref , and a program of PID-parameter adjustment was also given. Zeng et al employed the autoregressive integrated moving average model to establish the model of CCS and a generalized model predictive control (MPC) technique was then provided to accomplish the optimization control of coal-fired units. As a result of the development of data-driven technologies, data-driven-based predictive control algorithms are broadly utilized in the optimization control of coal-fired power plants .…”

Section: Introductionmentioning

confidence: 99%

Integration Scheme for Economic Load Dispatching and Optimization Control in Coal-Fired Plants Based on Sparse Gaussian Process Model and Deep Reinforcement Learning

Dai,

Chang,

Wang

et al. 2023

Ind. Eng. Chem. Res.

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The economic load dispatching and optimization control of coal-fired plants are of utmost importance to achieve carbon peaking and carbon neutrality for China. This article proposes an integrated scheme for combining the economic load dispatching and optimization control of power plants as a whole. In this scheme, both dynamic cost and static cost are employed to develop the model of plant-level economic load dispatching compared with the traditional method in that only static cost is considered. Specifically, the average coal consumption rate under steady-state operation conditions is applied to represent the static cost, and the sparse Gaussian process model that can effectively balance the modeling accuracy and the modeling cost is employed to establish the coal consumption rate model, faced with the big data characteristics of the historical operation data of coal-fired units. To properly evaluate dynamic cost, the Q function that comes from the process knowledge-assisted deep reinforcement learning control method is applied, according to the definition of the Q function and the designed reward function in the reinforcement learning. Moreover, different optimization strategies are designed for operators to choose according to actual dispatching requirements. Finally, the simulation cases that are similar to the actual dispatching scenarios are designed to verify the feasibility and effectiveness of the proposed integrated scheme. The simulation results show that compared with the traditional dispatching method, the proposed integration load dispatching and optimization control method in this paper can improve both the dynamic performance of transient conditions and the economic benefits of steady-state conditions and enhance the safety and economy of power plants.

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Optimization control for the coordinated system of an ultra-supercritical unit based on stair-like predictive control algorithm

Cited by 31 publications

References 16 publications

Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer

Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer

NOx concentration prediction in coal-fired power plant based on CNN-LSTM algorithm

Integration Scheme for Economic Load Dispatching and Optimization Control in Coal-Fired Plants Based on Sparse Gaussian Process Model and Deep Reinforcement Learning

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