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.

Neural Network Adaptive Control of Free Floating Space Robot with Actuator Saturation

2014 ◽  
Vol 590 ◽  
pp. 380-385 ◽  
Author(s):  
Guo Liang Zhang ◽  
Ting Lei ◽  
Fan Yang ◽  
Zhuang Cai
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Control Strategy ◽  
Actuator Saturation ◽  
Space Robot ◽  
Global Approximation ◽  
Neural Network Approach ◽  
The Neural Network ◽  
On Line ◽  
The Stability

This paper proposes an adaptive neural network law for trajectory tracking of a class of free-floating space robot with actuator saturation. Using neural network with global approximation, the control strategy design an on-line real time adaptive learning law to approach the uncertain model and the actuator saturation nonlinearity. The neural network approach errors and outside disturbance can be eliminated by a robust controller.The control strategy need not depend on the model, and can be used under actuator saturation.The control strategy can guarantee the stability of system and the asymptotic convergence of tracking errors based on the Lyapunov’s theory. The simulation results indicate that the proposed strategy can effectively work with actuator saturation.

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.

Identification and Control of a Benchmark Flexible Structure using Piezoelectric Actuators and Sensors

2003 ◽  
Vol 9 (12) ◽  
pp. 1401-1420
Author(s):  
Chun-Liang Lin ◽  
Gean-Pao Lee ◽  
Van-Tsai Liu
Keyword(s):  
Neural Network ◽  
Linear Time ◽  
Flexible Structure ◽  
Neural Network Approach ◽  
Linear Time Invariant ◽  
Linear Control Theory ◽  
The Neural Network ◽  
Linear Differential ◽  
Linear Time Invariant System ◽  
And Control

In this paper we propose a neural network approach for the identification and control of a benchmark flexible structure: a thin simply-supported plate with bonded piezoelectric film actuators and sensors. A specific linear differential inclusion is developed for a class of multilayer feedforward networks. With this technique, it is shown that the plant model identified by the neural network can be represented as a linear time-invariant system so that traditional or advanced linear control theory can be directly applied to design the stabilizing flexible structure controller.

Investigation of the possibility of reconstructing strain diagrams using the neural network approach

2011 ◽  
Vol 47 (15) ◽  
pp. 1689-1695
Author(s):  
M. B. Bakirov ◽  
O. A. Mishulina ◽  
I. A. Kiselev ◽  
I. A. Kruglov
Keyword(s):  
Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network

scholarly journals Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control

2021 ◽  
Vol 13 (6) ◽  
pp. 3235
Author(s):  
J. Enrique Sierra-García ◽  
Matilde Santos
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Control Strategy ◽  
Neural Control ◽  
Mapping Function ◽  
Energy System ◽  
Lookup Table ◽  
Hybrid Strategy ◽  
Pitch Control ◽  
The Neural Network

Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One of them is the control of the wind turbine in order to improve its energy efficiency. In this work, a new hybrid pitch-control strategy is proposed that combines a lookup table and a neural network. The table and the RBF neural network complement each other. The neural network learns to compensate for the errors in the mapping function implemented by the lookup table, and in turn, the table facilitates the learning of the neural network. This synergy of techniques provides better results than if the techniques were applied individually. Furthermore, it is shown how the neural network is able to control the pitch even if the lookup table is poorly designed. The operation of the proposed control strategy is compared with the neural control without the table, with a PID regulator, and with the combination of the PID and the lookup table. In all cases, the proposed hybrid control strategy achieves better results in terms of output power error.

Optimizing a Modular Half-Speed Synchronous Generator and Implementing Its Voltage Control System Based on a Neural Network

2021 ◽  
Vol 7 (7) ◽  
pp. 61-70
Author(s):  
Andrey A. TATEVOSYAN ◽  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnets ◽  
Magnetic System ◽  
Voltage Control ◽  
Synchronous Generator ◽  
Voltage Regulation ◽  
Design Parameters ◽  
Design Scheme ◽  
The Neural Network

A method for optimizing the parameters of a modular half-speed synchronous generator with permanent magnets (PMSG) and the generator voltage control system with a neural network-based algorithm are proposed. The basic design scheme of the modular half-speed PMSG is considered, which features a compact layout of the generator main parts, thereby ensuring the optimal use of the working volume, smaller sizes of the magnetic system, and smaller mass of the active materials used in manufacturing the machine. Owing to the simple and reliable design of the generator, its output parameters can be varied in a wide range with using standard electrical circuits for voltage stabilization and current rectification along with an additional voltage regulation unit. Owing to this feature, the design scheme of the considered generator has essential advantages over the existing analogs with a common cylindrical magnetic core. In view of these circumstances, the development of a high-efficient modular half-speed PMSG as an autonomous DC power source is of both scientific and practical interest; this generator can be used to supply power to a large range of electricity consumers located in rural areas, low-rise residential areas, military communities, allotments etc. In solving the problem of optimizing the generator’s magnetic system, the main electrical machine analysis equation is obtained. The optimal ratios of the winding wire mass to the mass of permanent magnets and of the PM height to the air gap value for achieving the maximum specific useful power output have been determined. An analytical correlation between the optimal design parameters of a half-speed modular PMSG and its power performance parameters has been established. The expediency to develop a neural network-based control system is shown. The number of load-bearing modules of the half-speed PMSG is determined depending on the wind velocity, load factor and the required output voltage. The neural network was trained on the examples of a training sample using a laboratory test bench. The neural network was implemented in the MatLab 2019b environment by constructing a synchronous generator simulation model in the Simulink software extension. The possibility of using the voltage control system of a half-speed modular PMSG with a microcontroller for operation of the neural network platform of the Arduino family (ArduinoDue) independently of the PC is shown.

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