scholarly journals Multifunctional PID Neuro-Controller for Synchronous Generator

2016 ◽  
Vol 9 ◽  
pp. 1-4
Author(s):  
Hocine Tiliouine
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Control ◽  
Control Strategy ◽  
Synchronous Generator ◽  
Generator System ◽  
Electromechanical Oscillations ◽  
Generator Voltage ◽  
Adaptive Control Strategy ◽  
Neuro Controller

This paper deals with a PID Neuro-Controller (PIDNC) for synchronous generator system. The controller is based on artificial neural network and adaptive control strategy. It ensures two functions: maintaining the generator voltage at its desired value and damping electromechanical oscillations. The performance of the proposed controller is evaluated on the basis of simulation tests. A comparative study of the results obtained with PIDNC and those with conventional PID was performed.

scholarly journals VSG damping adaptive adjustment based on BP neural network

2021 ◽  
Vol 2121 (1) ◽  
pp. 012036
Author(s):  
Mengzhao Zhang ◽  
Chunlin Guo
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Control Object ◽  
Synchronous Generator ◽  
Moment Of Inertia ◽  
Learning Ability ◽  
Adaptive Control Strategy ◽  
Virtual Synchronous Generator

Abstract The moment of inertia and damping of virtual synchronous generator (VSG) can be adjusted flexibly, which also has a significant impact on the transient performance of VSG. Constant damping or moment of inertia can not reduce frequency overshoot and fast response performance, so it is necessary to introduce adaptive damping control. Based on universal approximation theorem, BP neural network can fit continuous nonlinear function well. At the same time, it has the advantages of simple algorithm, powerful learning ability and fast learning speed. Based on the characteristics of the control object, the BP neural network is improved and a new adaptive control strategy is designed. The strategy uses improved BP neural network to adjust VSG virtual damping D online. Python-MATLAB-Simulink was used for co-simulation, BP neural network algorithm was integrated into the control object to establish an adaptive simulation model, and the proposed control strategy was simulated and verified. Simulation results show that the adaptive control strategy can eliminate overshoot and respond quickly when the frequency and active power of virtual synchronous generator change.

scholarly journals Application of Nonlinear Adaptive Control in Temperature of Chinese Solar Greenhouses

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1582
Author(s):  
Yonggang Wang ◽  
Yujin Lu ◽  
Ruimin Xiao
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Radial Basis Function ◽  
Control Strategy ◽  
Basis Function ◽  
Adaptive Controller ◽  
Strong Nonlinearity ◽  
Nonlinear Adaptive Control ◽  
Radial Basis ◽  
Adaptive Control Strategy

The system of a greenhouse is required to ensure a suitable environment for crops growth. In China, the Chinese solar greenhouse plays a crucial role in maintaining a proper microclimate environment. However, the greenhouse system is described with complex dynamic characteristics, such as multi-disturbance, parameter uncertainty, and strong nonlinearity. It is difficult for the conventional control method to deal with the above problems. To address these problems, a dynamic model of Chinese solar greenhouses was developed based on energy conservation laws, and a nonlinear adaptive control strategy combined with a Radial Basis Function neural network was presented to deal with temperature control. In this approach, nonlinear adaptive controller parameters were determined through the generalized minimum variance laws, while unmodeled dynamics were estimated by a Radial Basis Function neural network. The control strategy consisted of a linear adaptive controller, a neural network nonlinear adaptive controller, and a switching mechanism. The research results show that the mean errors were 0.8460 and 0.2967, corresponding to a conventional PID method and the presented nonlinear adaptive scheme, respectively. The standard errors of the conventional PID method and the nonlinear adaptive control strategy were 1.8480 and 1.3342, respectively. The experimental results fully prove that the presented control scheme achieves better control performance, which meets the actual requirements.

Research on adaptive control strategy of hydraulic mechanical continuously variable transmission of a cotton picker

2020 ◽  
Vol 234 (17) ◽  
pp. 3335-3345
Author(s):  
Xin Zhao ◽  
Xiangdong Ni ◽  
Qi Wang ◽  
Mingxi Bao ◽  
Sheng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Strategy ◽  
Basis Function ◽  
Continuously Variable Transmission ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Cotton Picker ◽  
Variable Transmission ◽  
Adaptive Control Strategy

Hydraulic mechanical continuously variable transmission has the advantages of good hydraulic stepless speed regulation performance and high efficiency of mechanical transmission, which can achieve the optimal matching of transmission system load and power source. In this article, an adaptive control strategy based on radial basis function neural network and proportional–integral–derivative control was proposed. The speed compound control method was used to solve the problems that the output speed of the hydraulic mechanical continuously variable transmission system was poor at the variable speed input and was difficult to control. The throttle opening and the engine speed were used as controller inputs. The pump–motor's displacement ratio and the output speed were used as controller outputs. Finally, the output speed of the cotton picker was stably controlled. Simulation and experimental results show that the transmission can quickly respond to the target speed and had little fluctuation based on different initial input speeds. The control strategy had good control precision and robustness. Compared with the traditional proportional–integral–derivative algorithm, the average steady-state error of the system output speed was controlled between ±0.0125%. The proposed algorithm based on radial basis function neural network proportional–integral–derivative adaptive control strategy had obvious control effect, and the stability of the speed output of the system was improved under the nonlinear input complex conditions. It provided research for the speed ratio adjustment and control of the hydraulic mechanical continuously variable transmission of the cotton picker.

scholarly journals VSG-Based Parameter Adaptive Control Strategy

2021 ◽  
Vol 257 ◽  
pp. 02041
Author(s):  
Guo Jianyi ◽  
Fan Youping
Keyword(s):  
Adaptive Control ◽  
Dynamic Response ◽  
Control Strategy ◽  
Control Strategies ◽  
Synchronous Generator ◽  
Frequency Change ◽  
Control Effect ◽  
Damping Characteristics ◽  
Adaptive Control Strategy ◽  
Virtual Synchronous Generator

As a large number of converters composed of power electronic devices are connected to the grid, power system has gradually decreased stability. How to increase dynamic response of the converter has become one of the research hotspots. Virtual synchronous generator technology (VSG) can endow the converter with moment of inertia and damping characteristics, thereby enhancing dynamic response, but the traditional VSG technology cannot achieve the optimal control effect. To solve this problem, an adaptive control strategy is proposed, which takes logical combination of system angular velocity and frequency change as the real-time change condition, with exponential function as the change expression. Finally, this paper uses MATLAB / Simulink to compare the method in this paper with several existing typical control strategies.

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