The Modeling of Steam Turbine Speed Control System Based on Radial Basis Function Neural Network

2010 ◽  
Vol 139-141 ◽  
pp. 1822-1826
Author(s):  
Yong Ren ◽  
Tao Yang ◽  
Wei Gao ◽  
Yang Hai Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Speed Control ◽  
Power System Stability ◽  
System Stability ◽  
Stability Studies ◽  
Speed Control System ◽  
Turbine Speed

The model of speed control system plays an important role in power system stability studies, the non-linear properties prevent us from getting accurate mechanism model. In this paper, the radial basis function neural network with self-structuring and fast convergence is used in the modeling of steam turbine speed control system in the modeling process, also, this paper presents a method which combines particle swarm optimization algorithm and least-squares algorithm for the neural network’s training, it has the property of high accuracy and fast convergence, after training, the proposed model and related training algorithm are verified by the test data of one power plant, it has proved that the neural network can be used in the modeling of the speed control system for the power system stability studies.

Study on the Speed Control System of Hydraulic Pump-Motor Based on PID Neural Network

2012 ◽  
Vol 542-543 ◽  
pp. 759-764
Author(s):  
Di Wu ◽  
Jin Yao ◽  
Yong Jing Huang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Dynamic Property ◽  
Speed Control ◽  
Theoretical Models ◽  
Hydraulic Pump ◽  
Transient Time ◽  
Neural Network Controller ◽  
Speed Control System ◽  
Pid Neural Network

Theoretical models of hydraulic pump-motor and electro-hydraulic servo control systems of hydraulic pumps were established and the system transfer function was derived In view of bad dynamic property, small damp, long transient time, low precision, nonlinearity and etc existing in the speed control system of Hydraulic Pump-Motor, a controller based on PID Neural Network was designed. The simulation results showed that the PID Neural Network controller has better dynamic property, load disturbance adaptability , robustness and cranking ability than traditional PID controller.

Research on the Hydropower Frequency Control Strategy for Power Delivery Grid Splitting

2013 ◽  
Vol 392 ◽  
pp. 641-645 ◽  
Author(s):  
Li Meng ◽  
Hao Wang ◽  
Jian She Tian
Keyword(s):  
Control System ◽  
Frequency Control ◽  
Power Grid ◽  
Speed Control ◽  
Power Delivery ◽  
Area Measurement ◽  
Frequency Regulation ◽  
Improvement Program ◽  
Speed Control System ◽  
Turbine Speed

After power delivery grid splitting off from the main power grid, there is the risk of over frequency generator tripping leading to low frequency load shedding action. Regulation characteristic of hydro-turbine speed control system for regional power grid is analyzed by using the measured parameters of hydropower. The turbine speed control effect is analyzed without lower limit of primary frequency regulation. Frequency control improvement program of hydropower generating units in isolated power system is proposed based on wide area measurement system. The scheme uses WAMS to collect power information as governor PID output lead, and to calculate power target into the speed control system.

Modeling a Pneumatic Turbine Speed Control System

1995 ◽  
Vol 122 (1) ◽  
pp. 222-225 ◽  
Author(s):  
Eric R. Upchurch ◽  
Hung V. Vu
Keyword(s):  
Control System ◽  
Speed Control ◽  
Speed Control System ◽  
Turbine Speed

[S0022-0434(00)01001-7]

The Application of PSO-BP Neural Network PID Controller in Variable Frequency Speed Regulation System

2014 ◽  
Vol 599-601 ◽  
pp. 1090-1093 ◽  
Author(s):  
Chun Hua Li ◽  
Shao Xiong Xu ◽  
Yang Xie ◽  
Jie Zhao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Speed Control ◽  
Regulation System ◽  
Particle Swarm Algorithm ◽  
Variable Frequency ◽  
Speed Control System ◽  
Neural Network Pid

Variable frequency speed control system hold good stability,more efficient,more energy conservation etc, so it has been widely used in the industrial areas,but the control strategy of traditional was difficult to achieve the desired control effect.This paper adopt particle swarm algorithm and BP neural network to construct the PID controller of PSO-BP neural network , the M-Files of PSO-BP neural network PID based on MATLAB through S-Function, and the mode of PSO-BP neural network PID variable frequency speed control system was established in SIMULINK platform.Simulation results show that the controller hold well robustness, follow and stability,and the dynamic characteristics of the original system was improved, the application value of this method in the variable frequency speed control system was proved.

scholarly journals Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1672
Author(s):  
Norhaliza Abdul Wahab ◽  
Nurazizah Mahmod ◽  
Ramon Vilanova
Keyword(s):  
Neural Network ◽  
Control System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Membrane Fouling ◽  
Internal Model ◽  
Closed Loop ◽  
Internal Model Control ◽  
Permeate Flux ◽  
Model Control

This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives the information (outputs) of permeate flux and trans-membrane pressure (TMP). The palm oil mill effluent is used as an influent preparation to depict fouling phenomenon in the membrane filtration process. From the experiment, membrane fouling potential is observed from flux decline pattern, with a rapid increment of TMP (above 200 mbar). Membrane fouling is a complex process and the available models in literature are not designed for control system (filtration performance). Therefore, this work proposes an aeration fouling control strategy to measure the filtration performance. The artificial neural networks (Feed-Forward Neural Network—FFNN, Radial Basis Function Neural Network—RBFNN and Nonlinear Autoregressive Exogenous Neural Network—NARXNN) are used to model dynamic behaviour of flux and TMP. In this case, only flux is used in closed loop control application, whereby the TMP effect is used for monitoring. The simulation results show that reliable prediction of membrane fouling potential is obtained. It can be observed that almost all the artificial neural network (ANN) models have similar shape with the actual data set, with the highest accuracy of more than 90% for both RBFNN and NARXN. The RBFNN is preferable due to simple structure of the network. In the control system, the RBFNN IMC depicts the highest closed loop performance with only 3.75 s (settling time) for setpoint changes when compared with other controllers. In addition, it showed fast performance in disturbance rejection with less overshoot. In conclusion, among the different neural network tested configurations the one based on radial basis function provides the best performance with respect to prediction as well as control performance.

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