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 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-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.

scholarly journals An Islanding Detection and Control Strategy to Realize the Stable and Autonomous Operation of Microgrids Using the Virtual Synchronous Generator

2021 ◽  
Vol 9 (11) ◽  
pp. 1315-1333
Author(s):  
Illuru Sree Lakshmi
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Synchronous Generator ◽  
Islanding Detection ◽  
Neural Network Approach ◽  
Autonomous Operation ◽  
The Neural Network ◽  
Control Stability ◽  
And Control ◽  
Virtual Synchronous Generator

Abstract: An islanding detection and based control strategy is created in this exploration to accomplish the steady and independent activity of microgrids using the neural network based Virtual Synchronous Generator (VSG) idea during unplanned grid reconfigurations . Maybe of utilizing a design-orientedmethodology, this paper gives a rigorous and extensive hypothetical investigation and reaches a concise conclusion that is easy to execute and successful even in complex situations. Based on the results of the mutation sequence and voltage wavering, a neural network based islanding identification calculation is proposed, which requires less constraint strategy. The proposed neural network approach outperforms the thefrequency measured passive detection method in terms of detection speed and reliability. Broad recreations affirm the reasonableness of the proposed islanding location and control methodology. Additionally, think about the results of the reproductions for the PI regulator, fluffy organizations, and neural organizations. Keywords: Virtual Synchronous Generator, Islanding detection, Islanding operation, Droop control, Stability, Microgrids.

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.

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