Performance Analysis of Reheat Steam Temperature Control System of Thermal Power Unit Based on Constrained Predictive Control

Li, Xiaoli; Liu, Jian; Wang, Kang; Wang, Fuqiang; Li, Yang

doi:10.1155/2019/9361723

Cited by 4 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A radial basis function (RBF) kernel was selected as the kernel function in the SVM model. Fivefold cross-validation combined with the grid search method was introduced to determine the kernel parameter g and plenty factor C. In total, 208 pairs of exponentially growing sequences (g, C) were attempted, that is, 13 . After optimization, g was determined to be 0.0625, and C was determined to be 1024.…”

Section: Comparison With Different Modelling Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Modelling and regulation of steam temperatures of a 1000‐MW double‐reheat boiler with long short‐term memory

Zhu,

Tan,

et al. 2023

IET Control Theory & Appl

View full text Add to dashboard Cite

With the increase in the proportion of new energy power generation in grids, there is an urgent need for the flexible operation of coal‐fired power plants. However, as the most efficient commercial coal‐fired power generation technology, the flexibility of double‐reheat ultra‐supercritical units is limited due to the steam temperature fluctuation in transient processes. To inhibit the steam temperature fluctuations, a data‐driven method, called long short‐term memory (LSTM) was introduced to model the dynamics of the steam temperatures of a 1000‐MW double‐reheat coal‐fired boiler. Moreover, an advanced controller was proposed by combining the LSTM‐based dynamic model and the model predictive control architecture. The results demonstrate that the LSTM model can be used to capture the time delay, multivariable coupling and non‐linear dynamics of the steam temperatures and the proposed controller exhibits excellent performance in steam temperature regulation. The superheated steam temperature fluctuation decreases from 15.4 to 9.8°C, the primary reheat temperature fluctuation decreases from 34.6 to 8.3°C, and the secondary reheat temperature fluctuation decreases from 41.4 to 8.8°C. The proposed method has good potential to stabilize steam temperatures during transient processes of wide‐range load cycling, and finally improves the flexibility of the double‐reheat ultra‐supercritical unit.

show abstract

Section: Comparison With Different Modelling Methodsmentioning

confidence: 99%

“…Furthermore, the extra reheat steam circulation system enhances the inertia and delay during transients [12]. Thus, the regulation of steam temperatures of DR USC units is more difficult than that of single‐reheat units [13].…”

Section: Introductionmentioning

confidence: 99%

Modelling and regulation of steam temperatures of a 1000‐MW double‐reheat boiler with long short‐term memory

Zhu,

Tan,

et al. 2023

IET Control Theory & Appl

View full text Add to dashboard Cite

show abstract

“…The reheat steam temperature has the characteristics of large delay and large inertia and is affected by many factors, such as unit load, coal quality, desuperheating water flow, fouling and slagging, coal-air distribution, and excess air coefficient. The reheat steam temperature object exhibits nonlinear and timevarying characteristics under various disturbances, making it difficult to control (Li et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…LSTM is an important variant of traditional RNN. It is augmented by adding recurrent gates called "forget gates" as shown in Figure 1, where information can be selectively remembered or forgotten, making it effectively solve problems with long-time dependence (Li et al, 2019). It is observed as the most effective RNN in industrial applications.…”

Section: Brief Introduction On Lstm Algorithmmentioning

confidence: 99%

Multi-Step Ahead Prediction of Reheat Steam Temperature of a 660 MW Coal-Fired Utility Boiler Using Long Short-Term Memory

Tan

Zhu

et al. 2022

Front. Energy Res.

View full text Add to dashboard Cite

With increases in the penetration of renewables in grids, there is an increasing demand for coal-fired power plants to operate flexibly. Regulation of reheat steam temperature is of great importance for the safe and efficient operation of coal-fired power plants. However, the difficulty of reheat steam temperature regulation increases largely during flexible operation due to the large delay and nonlinear properties, especially those units designed to shoulder base load and with limited regulating strategy. A multistep prediction model on the reheat steam temperature of a 660-MW coal-fired utility boiler was developed based on long short-term memory. The results show that the multistep prediction model performs well. The average root mean square error and mean absolute percentage error values of the five-step prediction results are less than 0.52°C and 0.07%, respectively. The correlation coefficients of the five-step predictions are all greater than 0.95. With a sample interval of 30 s, the model provides an accurate prediction of reheat steam temperature within 2.5 min, which could supply an important reference for the reheat steam temperature regulation.

show abstract

“…With the large-scale renewable energy grid-connected power generation, it brings greater volatility to the power system. Such fluctuations bring great difficulties to the control of SST and affect the safe and economic operation of units [1]. The relevant study shows that the unit efficiency will increase one percent while SST increase 5 °C.…”

Section: Introductionmentioning

confidence: 99%

State variable-fuzzy prediction control strategy for superheated steam temperature of thermal power units

Shi

Yao

et al. 2021

THERM SCI

View full text Add to dashboard Cite

With the large-scale grid connection of new energy power, the random fluctuation existing in the power system is intensified, which leads to frequent fluctuation of load instructions of thermal power units. It is of great significance to improve the variable load performance of the coal-fired units. It is more difficult to control the superheated steam temperature (SST). In order to improve the control performance of SST, a state variable fuzzy predictive control method is proposed in this paper. Firstly, Takagi-Sugeno fuzzy state observer is used to approximate the non-linear plant of the SST. At the same time, based on the state observer, a fuzzy state feedback controller is designed to improve its dynamic characteristics. Thirdly, based on the extended predictive model of the state feedback controller, a model predictive controller is designed to realize the SST tracking control. Dynamic simulation shows the effectiveness of the strategy.

show abstract

Performance Analysis of Reheat Steam Temperature Control System of Thermal Power Unit Based on Constrained Predictive Control

Cited by 4 publications

References 17 publications

Modelling and regulation of steam temperatures of a 1000‐MW double‐reheat boiler with long short‐term memory

Modelling and regulation of steam temperatures of a 1000‐MW double‐reheat boiler with long short‐term memory

Multi-Step Ahead Prediction of Reheat Steam Temperature of a 660 MW Coal-Fired Utility Boiler Using Long Short-Term Memory

State variable-fuzzy prediction control strategy for superheated steam temperature of thermal power units

Contact Info

Product

Resources

About