scholarly journals On Variable-Universe Fuzzy Control for Drive Chain of Front-End Speed Regulated Wind Generator

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Hongwei Li ◽  
Kaide Ren ◽  
Haiying Dong ◽  
Shuaibing Li
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Fuzzy Controller ◽  
Synchronous Generator ◽  
Variable Universe ◽  
Neural Network Controller ◽  
Front End ◽  
Network Controller ◽  
Drive Chain

The rapid development of wind generation technology has boosted types of the new topology wind turbines. Among the recently invented new wind turbines, the front-end speed regulated (FSR) wind turbine has attracted a lot of attention. Unlike conventional wind turbine, the speed regulation of the FSR machines is realized by adjusting the guide vane angle of a hydraulic torque converter, which is converterless and much more grid-friendly as the electrically excited synchronous generator (EESG) is also adopted. Therefore, the drive chain control of the wind turbine owns the top priority. To ensure that the FSR wind turbine performs as a general synchronous generator, this paper firstly modeled the drive chain and then proposed to use the variable-universe fuzzy approach for the drive chain control. It helps the wind generator operate in a synchronous speed and outperform other types of wind turbines. The multipopulation genetic algorithm (MPGA) is adopted to intelligently optimize the parameters of the expansion factor of the designed variable-universe fuzzy controller (VUFC). The optimized VUFC is applied to the speed control of the drive chain of the FSR wind turbine, which effectively solves the contradiction between the low precision of the fuzzy controller and the number of rules in the fuzzy control and the control accuracy. Finally, the main shaft speed of the FSR wind turbine can reach a steady-state value around 1500 rpm. The response time of the results derived using VUFC, compared with that derived from a neural network controller, is only less than 0.5 second and there is no overshoot. The case study with the real machine parameter verifies the effectiveness of the proposal and results compared with conventional neural network controller, proving its outperformance.

Engineering Calculation And Algorithm Of Adaptation Of Parameters Of A Neuro-Fuzzy Controller

2021 ◽  
Vol 03 (09) ◽  
pp. 41-49
Author(s):  
I.H. Siddikov ◽  
◽  
P.I. Kalandarov ◽  
D.B., Yadgarova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Rule Base ◽  
Transient Time ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Neuro Fuzzy ◽  
Control Scheme

As part of the study, a control scheme with the adaptation of the coefficients of the neuron-fuzzy regulator implemented. The area difference method used as a training method for the network. It improved by adding a rule base, which allows choosing the optimal learning rate for individual neurons of the neural network. The neural network controller applied as a superstructure of the PID controller in the process control scheme. The dynamic object can function in different modes. This technological process operates in different modes in terms of loading and temperature setpoints. Because of experiments, the power consumption and the amount of time required maintaining the same absorption process, using a conventional PID controller and a neural-network controller evaluated. It concluded that the neuro-fuzzy controller with a superstructure reduced the transient time by 19%.

Sign in / Sign up

Export Citation Format

Share Document