scholarly journals Research on speed regulation system of water turbine generator unit based on PID neural network

2020 ◽  
Vol 525 ◽  
pp. 012101
Author(s):  
Ping Yu ◽  
Yue Yu ◽  
Zhiqiang Ren ◽  
Congbing Wu ◽  
Yubao Xu
Keyword(s):  
Neural Network ◽  
Regulation System ◽  
Turbine Generator ◽  
Speed Regulation ◽  
Water Turbine ◽  
Generator Unit ◽  
Pid Neural Network

Study on Intelligent Controller of Permanent Magnet Synchronous Motor

2013 ◽  
Vol 764 ◽  
pp. 149-153
Author(s):  
H. Ji ◽  
Zhi Yong Li
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Pid Controller ◽  
Design Method ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Regulation System ◽  
Speed Regulation ◽  
Neural Network Controller ◽  
Adaptive Ability

This paper puts forward a novel design method of controller based on BP neural network, which is applied to the permanent magnet synchronous motor (PMSM) double closed loop speed regulation system of speed regulator, by using the neural network controller instead of traditional PID controller. It applies the nonlinear adaptive ability of neural network for optimizing the control parameters of PID controller for PMSM. The simulation model was established in Matlab/Simulink. The simulation results indicate that the neutral network PID controller, compared with the traditional PID, has strong robustness and adaptive ability to the model and environments, indicating the good dynamic and static characteristics and control effects.

Speed-Regulation System of PMLSM Based on BP Neural Network PID Control

2012 ◽  
Vol 466-467 ◽  
pp. 1217-1221 ◽  
Author(s):  
Fu Zhong Wang ◽  
Qiong Xia Yu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Dynamic Performance ◽  
Regulation System ◽  
Time Varying ◽  
Bp Network ◽  
Speed Controller ◽  
Speed Regulation ◽  
Neural Network Pid

For the characteristics of permanent magnet linear synchronous motor (PMLSM) hoisting system’s nonlinear, time-varying and vulnerable to disturbance, based on the established PMLSM d-q axis dynamic model, designed of an improved BP neural network PID speed controller. Modified the fixed learning rate in BP neural network to adaptive adjustable, and added the momentum to reduce the oscillation tendency in the learning process, greatly improved the convergence speed and avoided the network into a local minimum. Compared with the simulations of traditional PID and the improved BP network PID speed controller, the results showed that the improved BP neural network PID speed controller had the high quality and it can make the system with better dynamic performance and robustness.

Reform of Excitation System on the Domestic 600MW Subcritical Turbine Generator Unit

2014 ◽  
Vol 986-987 ◽  
pp. 1837-1841
Author(s):  
Zhi Ling Han
Keyword(s):  
Regulation System ◽  
Turbine Generator ◽  
Excitation System ◽  
Energy Group ◽  
Field Situation ◽  
Start Up ◽  
Exciting System ◽  
Generator Unit ◽  
The Stability ◽  
Digital Excitation

Analog Excitation regulation system is applied in No.3 turbine generator unit of Harbin No.3 power plant, Huadian Energy Group, and the system has several problems such as high failure rate, poor regulating performance and low level automation. As a solution, the static self-shunt excitation system is used to replace the analog excitation regulation system, UNITROL 5000 all-digital excitation system is adopted. With specific field situation, the paper introduces the structure and application of the system in details. Compared with the conventional A.C. exciting system, the all-digital excitation system has effectively improved the stability and reliability of the generator, with the advantages of more flexibility of the start-up modes and undisturbed switching. It is also reliable and convenient for the switching of PSS additional regulation unit.

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