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

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4073
Author(s):  
Daogang Peng ◽  
Yue Xu ◽  
Huirong Zhao
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Optimization Model ◽  
Power Grid ◽  
Thermal Power ◽  
Automatic Generation ◽  
Performance Standard ◽  
Control Performance ◽  
Predictive Controller ◽  
Rolling Optimization

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.

Fuzzy Adaptive Direct Generalized Predictive Control Algorithm in the Application of AWC Control

2014 ◽  
Vol 945-949 ◽  
pp. 2529-2532
Author(s):  
Fang Chen Yin ◽  
Geng Sheng Ma ◽  
Ya Feng Ji ◽  
Jia Xue Yu ◽  
De Hao Gu ◽  
...  
Keyword(s):  
Predictive Control ◽  
Generalized Predictive Control ◽  
Continuous Mill ◽  
Control Performance ◽  
Hot Strip ◽  
Predictive Controller ◽  
Feedback Correction ◽  
Rolling Optimization ◽  
Pure Delay ◽  
Fuzzy Adaptive

Using the characteristics of prediction model, rolling optimization and feedback correction, a AWC system based on generalized predictive control was designed, and its control performance was simulated based on a hot strip continuous mill. The results show that generalized predictive controller achieves better control effects than the normal PID on response time and steady precision with matching model; when model mismatching is caused by inaccuracy of plastic coefficient and pure delay time, the normal PID is overshot or even oscillation, but the control performance of the generalized predictive controller is not influenced by model parameter variations .

scholarly journals Intelligent Frequency Control Strategy Based on Reinforcement Learning of Multi-Objective Collaborative Reward Function

2021 ◽  
Vol 9 ◽  
Author(s):  
Lei Zhang ◽  
Yumiao Xie ◽  
Jing Ye ◽  
Tianliang Xue ◽  
Jiangzhou Cheng ◽  
...  
Keyword(s):  
Large Scale ◽  
Learning Strategy ◽  
Model Simulation ◽  
Frequency Control ◽  
Power Grid ◽  
Automatic Generation ◽  
Performance Standard ◽  
Control Performance ◽  
Multi Objective ◽  
Reward Function

Large scale wind power integration into the power grid will pose a serious threat to the frequency control of power system. If only Control Performance Standard (CPS) index is used as the evaluation standard of frequency quality, it will easily lead to short-term centralized frequency crossing, which will affect the effect of intelligent Automatic Generation Control (AGC) on frequency quality. In order to solve this problem, a multi-objective collaborative reward function is constructed by introducing a collaborative evaluation mechanism with multiple evaluation indexes. In addition, Negotiated W-Learning strategy is proposed to globally optimize the solution of the objective function from multi dimensions, it avoids the poor learning efficiency of the traditional Greedy strategy. The AGC control model simulation of standard two area interconnected power grid shows that the proposed intelligent strategy can effectively improve the frequency control performance and improve the frequency quality of the system in the whole-time scale.

Hot Blast Stove Temperature Control System Based on Neural Network Predictive Control

2014 ◽  
Vol 709 ◽  
pp. 281-284 ◽  
Author(s):  
Yao Wu Tang ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Control Method ◽  
Delay System ◽  
Temperature Control System ◽  
Control Effect ◽  
Rolling Optimization ◽  
Hot Blast ◽  
Neural Network Predictive Control

Chain type coal-fired hot blast furnace boiler has a strong coupling, large delay, large inertia characteristics. Control effect of control method of mathematic modeling method and the classical routine of it is very difficult to produce the ideal. The predictive control theory combined with neural network theory. Through the model correction and rolling optimization control method of the system is good to overcome the effects of model error and time-varying process. The experimental results showed that neural network predictive control system is improved effectively the static precision and dynamic characteristic. It has better practicability of boiler temperature of this kind of large time delay system.

Explicit Indirect Predictive Control Algorithm in the Application of AWC Control

2014 ◽  
Vol 926-930 ◽  
pp. 1344-1347
Author(s):  
Fang Chen Yin ◽  
Geng Sheng Ma ◽  
Ya Feng Ji ◽  
Zhong Ping Li ◽  
Dian Hua Zhang
Keyword(s):  
Predictive Control ◽  
Delay Time ◽  
Control Algorithm ◽  
Continuous Mill ◽  
Control Performance ◽  
Hot Strip ◽  
Parameter Variations ◽  
Feedback Correction ◽  
Rolling Optimization ◽  
Pure Delay

Using the characteristics of prediction model, rolling optimization and feedback correction, a AWC system based on explicit indirect predictive control was designed, and its control performance was simulated based on a hot strip continuous mill. The results show that explicit indirect predictive control achieves better control effects than the normal PID on response time and steady precision with matching model; when model mismatching is caused by inaccuracy of plastic coefficient and pure delay time, the normal PID is overshot or even oscillation, but the control performance of the explicit indirect predictive control is not influenced by model parameter variations [1].

Research on the Supplementary Analysis System Based on CPS Standard

2012 ◽  
Vol 503-504 ◽  
pp. 1605-1608
Author(s):  
Hua Ting Tao ◽  
Li Li Ji
Keyword(s):  
Frequency Control ◽  
Power Grid ◽  
Influence Factors ◽  
Power Grids ◽  
Performance Standard ◽  
Control Performance ◽  
Control Performance Standard ◽  
Pros And Cons ◽  
Analysis System ◽  
Main Influence

CPS test standard has put forward new demands to the management of power grids. This paper analyzes the main influence factors of CPS, and introduces the way of building data warehouse and constructing supplementary analysis system on this basis, which can effectively help power grid workers to research the pros and cons in dispatch and operation. The application of the supplementary analysis system is helpful for Shanghai power grid to enhance AGC control and frequency control ability under the control performance standard.

scholarly journals Implementation of Neural Predictive Control To Distillation Column

REAKTOR ◽  
2017 ◽  
Vol 10 (1) ◽  
pp. 24
Author(s):  
S. Anwari
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Distillation Column ◽  
Controller Design ◽  
Fundamental Equation ◽  
Small Range ◽  
Unknown Parameters ◽  
Predictive Controller ◽  
Multivariable Models ◽  
Reliable Solution

This paper presents a neural predictive controller that is applied to distillation column. Distillation columns represent complex multivariable system, with fast and slow dynamics, significant interactions and directionality. A phenomenological model (i.e. a model derived from fundamental equation like mass and energy balance) of a distillation column is very complicated. For this reason, classical linear controller, such as PID (Proportional, Integral and Derivative) controller, will provide robustness only over relatively small range operation because of complexity and operation without lack of robustness. In this work, a neural network is developed for modeling and controlling a distillation column based on measured input-outputdata pairs. In distillation column, a neural network is trained on the unknown parameters of the system. The resulting implementationof the neural predictive controller is able to eliminate the most significant obstacles encountered in conventional predictive control application by facilitating  the development of complex multivariable models and providing a rapid, reliable solution to the control algorithm. Controller design and implementation are illustrated for a plant frequently referred to in the literature. Result are given for simulation experiments, which demonstrate the advantage of the neural based predictive controller both at the transient region and at the steady state region to overcome any overshoots.Keywords : neural predictive controller, distillation column, complex multivariable models

scholarly journals Hybrid Grey Wolf Optimization Nonlinear Model Predictive Control for Aircraft Engines Based on an Elastic BP Neural Network

2019 ◽  
Vol 9 (6) ◽  
pp. 1254 ◽  
Author(s):  
Lingfei Xiao ◽  
Min Xu ◽  
Yuhan Chen ◽  
Yusheng Chen
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Bp Neural Network ◽  
Aircraft Engine ◽  
Aircraft Engines ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Model Predictive Controller ◽  
Predictive Controller

In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this paper. Based on the acquired aircraft engines data, the elastic BP neural network is used to train the prediction model, and the grey wolf optimization algorithm is applied to improve the selection of initial parameters in the elastic BP neural network. The accuracy of network modeling is increased as a result. By introducing the logistics chaotic sequence, the individual optimal search mechanism, and the cross operation, the novel hybrid grey wolf optimization algorithm is proposed and then used in receding horizon optimization to ensure real-time operation. Subsequently, a nonlinear model predictive controller for aircraft engine is obtained. Simulation results show that, with constraints in the control signal, the proposed nonlinear model predictive controller can guarantee that the aircraft engine has a satisfactory performance.

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