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 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 An Adaptive Control Strategy for Dynamic Response of an Autonomous DC system

2021 ◽  
Author(s):  
Farshid Norouzi ◽  
Laura Ramirez Elizondo ◽  
Thomas Hoppe ◽  
Pavol Bauer
Keyword(s):  
Adaptive Control ◽  
Dynamic Response ◽  
Control Strategy ◽  
Dc System ◽  
Adaptive Control Strategy

Zone-Adaptive Control Strategy for a Multiperiodic Model of Risk

2007 ◽  
Vol 2 (2) ◽  
pp. 349-367 ◽  
Author(s):  
V. K. Malinovskii
Keyword(s):  
Adaptive Control ◽  
Control Strategy ◽  
Risk Model ◽  
Control Strategies ◽  
Control Approach ◽  
Adaptive Control Strategy

ABSTRACTThis paper is intended to illustrate the adaptive control approach in insurance. A zone-adaptive control strategy harmonising the requirements of principles of solvency and equity is considered in the simplistic framework of a diffusion multiperiodic risk model. Other works by the author set similar adaptive control strategies in a more realistic Poisson-exponential multiperiodic risk model. There is much scope for further generalisations.

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.

Development of an Adaptive Control Strategy in a Live Intersection Laboratory

1998 ◽  
Vol 1634 (1) ◽  
pp. 123-129 ◽  
Author(s):  
Eil Kwon ◽  
Yorgos J. Stephanedes
Keyword(s):  
Adaptive Control ◽  
Machine Vision ◽  
Control Strategy ◽  
Control Method ◽  
Detection System ◽  
Control Strategies ◽  
Field Evaluation ◽  
Current Status ◽  
Laboratory Environment ◽  
Adaptive Control Strategy

The current status of the Minnesota intersection laboratory and a new adaptive control strategy developed using the laboratory environment are presented in this paper. The laboratory is equipped with a machine-vision detection system with 6 cameras that are collecting detailed traffic data from a total of 110 virtual detectors. The new control method is based on the link-congestion index that quantifies the link-wide level of congestion, using the point measurements from traffic sensors (e.g., machine-vision detectors or conventional loops). Further, using the data collected from the laboratory, a new microscopic simulator was also developed to meet the specific needs for the laboratory environment. The current version of the simulator adopts a modified cellular automata approach with the simplified car-following model, which was developed and tested in this work. The evaluation results with the simulator indicated significant performance improvements of the new strategy over the pretimed and the current actuated-control strategies being operated in Minneapolis. Currently the control method is being refined for field evaluation at the intersection laboratory.

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