Recurrent Neural Network Supplementary Stabilization Controller for Automatic Voltage Regulator and Governor

2003 ◽  
Vol 31 (7) ◽  
pp. 693-707 ◽  
Author(s):  
TOMONOBU SENJYU ◽  
HIDEKI FUJITA
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Voltage Regulator ◽  
Automatic Voltage Regulator

scholarly journals An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator

2021 ◽  
Vol 19 (2) ◽  
pp. 136-144
Author(s):  
Widi Aribowo ◽  
Bambang Suprianto ◽  
I Gusti Putu Asto Buditjahjanto ◽  
Mahendra Widyartono ◽  
Miftahur Rohman
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Recurrent Neural Network ◽  
Optimization Method ◽  
Voltage Regulator ◽  
Backpropagation Neural Network ◽  
Automatic Voltage Regulator ◽  
Distributed Time Delay ◽  
Rotor Angle ◽  
Elman Recurrent Neural Network

The parasitism – predation algorithm (PPA) is an optimization method that duplicates the interaction of mutualism between predators (cats), parasites (cuckoos), and hosts (crows). The study employs a combination of the PPA methods using the cascade-forward backpropagation neural network. This hybrid method employs an automatic voltage regulator (AVR) on a single machine system, with the performance measurement focusing on speed and the rotor angle. The performance of the proposed method is compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation neural network (CFBNN), Elman recurrent neural network (E-RNN), focused time-delay neural network (FTDNN), and distributed time-delay neural network (DTDNN). The results show that the proposed method exhibits the best speed and rotor angle performance. The PPA-CFBNN method has the ability to reduce the overshoot of the speed by 1.569% and the rotor angle by 0.724%.

PID Parameters Tuning Based on Wavelet Neural Network for Automatic Voltage Regulator Control System

2012 ◽  
Vol 463-464 ◽  
pp. 1663-1667
Author(s):  
Hai Na Hu ◽  
Wu Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Power System ◽  
Synchronous Generator ◽  
Wavelet Neural Network ◽  
System Stability ◽  
Voltage Regulator ◽  
System Control ◽  
Automatic Voltage Regulator ◽  
Avr System

Automatic Voltage Regulator (AVR) was applied to hold terminal voltage magnitude of a synchronous generator at a specified level and its stability seriously affects the security of power system. PID control was applied for AVR system control, but the parameters of PID controller were hard to determine, to overcome this problem, some intelligent techniques should be taken. Wavelet Neural Network (WNN) was constrictive and fluctuant of wavelet transform and has self-study, self adjustment and nonlinear mapping functions of neural networks, so the structure of WNN and PID tuning with WNN was proposed, the tuning algorithm was applied into AVR control system, the simulation was taken with normal BP neural network and WNN, the efficiency and advantages of this control strategy was successfully demonstrated which can applied into AVR system for power system stability.

scholarly journals Focused Time Delay Neural Network For Tuning Automatic Voltage Regulator

2019 ◽  
Vol 7 (1) ◽  
pp. 34-43 ◽  
Author(s):  
Widi Aribowo
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Dynamic Networks ◽  
Voltage Regulator ◽  
Robust Performance ◽  
Matlab Simulink ◽  
Automatic Voltage Regulator ◽  
Avr System ◽  
Load Angle

This paper proposes a novel controller for automatic voltage regulator (AVR) system. The controller is used Focused Time Delay Neural Network (FTDNN). It does not require dynamic backpropagation to compute the network gradient. FTDNN AVR can train network faster than other dynamic networks. Simulation was performed to compare load angle (load angle) and Speed. The performance of the system with FTDNN-AVR has compared with a Conventional AVR (C-AVR) and RNN AVR. Simulations in Matlab/Simulink show the effectiveness of FTDNN-AVR design, and superior robust performance with different cases.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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