MOPSO-tuning of a threshold-based algorithm to start up and shut-down rapid-start units in AGC

Saboya, I.; Egido, Ignacio; Lobato, E.; Sigrist, Lukas

doi:10.1016/j.ijepes.2018.12.038

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…The irst step in cold start-up of steam turbine is to select the impulse parameters of steam turbine [14]. When the steam turbine starts in cold state, the main hot steam pressure and reheat steam pressure and temperature in front of the main steam valve shall meet the requirements of the startup curve provided by the steam turbine when it leaves the There should be a certain degree of superheat when the main steam of steam turbine enters.…”

Section: Cold Start-up and Grid Connection Process Of Steam Turbinementioning

confidence: 99%

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Working process of steam turbine and establishment of start-up model

Dong¹,

Sun²

2021

Int J Phys Res Appl

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In the research of steam turbine rotor, start-up optimization is a very key research problem. A series of start-up optimization research can greatly improve the start-up efficiency of steam turbine and the safety performance of the unit. The start-up optimization of steam turbine is inseparable from the analysis of the start-up process of steam turbine and the mathematical model of the startup process of steam turbine unit, because the optimization of steam turbine unit can be regarded as a function to find the optimal solution. This paper analyzes the start-up process of 300 MW steam turbine, analyzes the start-up process of steam turbine unit through the data used in the actual power plant, and gives the mathematical model of cold start-up of steam turbine according to the start-up process of steam turbine, so as to further study the start-up optimization of steam turbine. Finally, the optimization model is determined by several key parameters, which are three weight coefficients α1,α2,α3, the actual damage value Di and damage limit value Dlim, and the start-up time ti and total start-up time t0 of each stage.

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Section: Cold Start-up and Grid Connection Process Of Steam Turbinementioning

confidence: 99%

“…The distribution of temperature ield of rotor is uneven. Centrifugal force and steam pressure affect the mechanical stress and thermal stress of rotor respectively [14]. The accurate boundary conditions directly affect the calculation of temperature distribution and stress distribution.…”

Section: Establishing Boundary Conditionsmentioning

confidence: 99%

Working process of steam turbine and establishment of start-up model

Dong¹,

Sun²

2021

Int J Phys Res Appl

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“…(17) After feedback correction, the output of the prediction model is revised from y m (k + 1) to y p (k + 1) [38]. In Formula (11), h is the error correction coefficient [39], whose value is adjusted according to the actual operation of the specific system.…”

Section: (2) Feedback Correctionmentioning

confidence: 99%

“…38]. In Formula (11), h is the error correction coefficient [39], whose value is adjusted according to the actual operation of the specific system. The core idea of predictive control is to decompose a long-term optimal control problem into several short-term optimization problems.…”

Section: ) Neural Network Rolling Optimizationmentioning

confidence: 99%

Research on Intelligent Predictive AGC of a Thermal Power Unit Based on Control Performance Standards

Peng

Zhao

2019

Energies

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In order to satisfy the growing demands of control performance and operation efficiency in the automatic generation control (AGC) system of a grid, a novel, intelligent predictive controller, combined with predictive control and neural network ideas, is proposed and applied to the AGC systems of thermal power units. This paper proposes a Bayesian neural network identification model for typical ultra-supercritical thermal power units, which was found to be accurate and can be used as a simulation model. Based on the model, this paper develops an intelligent predictive control for the AGC of thermal power units, which improves unit load operation and constitutes a novel, closed-loop AGC structure based on online control performance standard (CPS) evaluations. Intelligent predictive control is mainly improved because the neural network rolling optimization model replaces the traditional rolling optimization model in the rolling optimization module. The simulation results indicate that the intelligent predictive controller developed in the two-area interconnected power grid under CPS can, on the one hand, improve the load tracking performance of AGC thermal power units, and, on the other hand, the controller has strong robustness. Whether the system parameters change considerably or the AGC has different grid disturbances, the new type of the loop AGC system can still sufficiently meet the control requirements of the power grid.Energies 2019, 12, 4073 2 of 23 in [10], and the combination of various control methods [11] in the AGC system to improve development of the AGC system to some degree. Nanda et al. [12] proposed using the genetic algorithm (GA) to optimize control of the AGC system. However, this method has some shortcomings, as GA only carries out crossover and mutation operations, and it is likely to fall into local optimum or premature convergence. This has resulted in a dramatic reduction in the efficiency and searchability of the algorithm. Gozde and Taplamacioglu [13] developed a gain-scheduling proportional-integral (PI) controller for a dead zone, nonlinear AGC system. The authors used the craziness-based particle swarm optimization algorithm to minimize the standard error and solution time of different objective functions. Rabindra Kumar Sahu et al. [14] changed the controller design problem into a controller optimization problem for automatic generation control (AGC) of multi-area power systems with diverse energy sources. The proposed teaching learning based optimization (TLBO) algorithm was employed to optimize the parameters of the PID controller, and the superiority of the proposed TLBO controller has been demonstrated by simulation comparison experiments.Appropriate models serve for excellent control algorithms. As one of the intelligent algorithms, model predictive control (MPC) is advantageous because of its unique model prediction, rolling optimization, and feedback correction structure [15,16]. An MPC algorithm was presented to control coal convey systems in a coal-fired power plant [17]. Ne...

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