Implementation of Probabilistic Neural Network (Pnn) Based Automatic Voltage Regulator (Avr) for Excitation Control System in Matlab

Bhutto, Azhar Ali; Chachar, Faheem Akhtar; Hussain, Muhammad Ishfaq; Bhutto, Darya Khan; Bakhsh, Sher Elahi

doi:10.1109/icomet.2019.8673416

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…There are various AI methods for optimizing PID parameters in an AVR system and each one has its own merits and demerits [5]. Neural network, fuzzy system, and neural-fuzzy logic were three famous AI techniques [6][7][8]. These methods suffer from the problems of convergence time, training process and tuning the membership function [9].…”

Section: Experimental Evaluation Of Optimal Tuning For Pidmentioning

confidence: 99%

Experimental Evaluation of Optimal Tuning for PID Parameters in an AVR System

Foroutan

Tootoonchian

2021

Scientia Iranica

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Automatic Voltage Regulator (AVR) is employed to stabilize the output voltage of the generators in the electric power plants. However, reliable performance of AVR depends on professional tuning of its PID controller's parameters. Therefore, different optimization algorithms are used to determine those parameters. The objective of the optimization is defined as minimizing the characteristics of transient step response such as settling time, rise time, overshoot, and steady state error. Then, to verify the optimization results, a simulator is built experimentally for AVR and PID system that can also be used for other studies on AVR systems. The experimental results are compared with those of MATLAB and Pspice Software. Close agreement between the simulation and experimental results confirms the success of the optimization.

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Section: Experimental Evaluation Of Optimal Tuning For Pidmentioning

confidence: 99%

Experimental Evaluation of Optimal Tuning for PID Parameters in an AVR System

Foroutan

Tootoonchian

2021

Scientia Iranica

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“…Many researchers are interested in the field of power system stability. Several researchers have updated the method to improve AVR performance such as Particle Swarm Optimization (PSO) [6][7][8], salp optimization algorithm [9,10], Teaching-Learning-Based Optimization (TLBO) [11,12], Cuckoo Search Algorithm [13,14], Sine-cosine algorithm [15,16], and neural network [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The application of neural network methods to AVR control also varies in types. Bhutto [18] has used Probabilistic Neural Network (PNN) as AVR control. Aribowo [19] has applied a Focused Time Delay Neural Network (FTDNN) which functions as an AVR control.…”

Section: Introductionmentioning

confidence: 99%

Slime Mould Algorithm Training Neural Network in Automatic Voltage Regulator

Aribowo

2022

Trends Sci

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The research is proposed a new method of artificial intelligence (AI) to control automatic voltage regulators. A neural network has improved using a metaheuristic method, namely the slime mould algorithm (SMA). SMA has an algorithm based on the mode of slime mold in nature. SMA has characteristics that use adaptive weights to simulate the process to generate feedback from the movement of bio-oscillator-based slime molds in foraging, exploring, and exploiting areas. The performance of the proposed method is focused on speed and rotor angle. To know the competence and potency of the proposed method, a comparison with feed-forward backpropagation neural networks (FFBNN), cascade-forward backpropagation neural networks (CFBNN), Elman-recurrent neural networks (E-RNN), focused time delay neural network (FTDNN), and Distributed Time Delay Neural Network (DTDNN) method are applied. It can be concluded that the proposed method has the best ability. The Proposed method has ability to reduce the overshoot speed with an average value of 0.78 % and the overshoot rotor angle with an average value of 2.134 %.

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“…Several computational methods have started to be used in AVR settings such as the genetic algorithm (GA) [5,6], teaching-learning-based optimization (TLBO) [7,8], sine cosine algorithm [9,10], world cup optimization [11,12], biogeography-based optimization [14], the Jaya optimization algorithm [15], global neighborhood algorithm [16], simulated annealing optimization algorithm [17], cuckoo search algorithm [18], firefly algorithm [19], whale optimization algorithm [20], and neural network [21,22]. This paper presents an analysis of the AVR performance, set up using a neural network based Fig.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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%.

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Implementation of Probabilistic Neural Network (Pnn) Based Automatic Voltage Regulator (Avr) for Excitation Control System in Matlab

Cited by 13 publications

References 8 publications

Experimental Evaluation of Optimal Tuning for PID Parameters in an AVR System

Experimental Evaluation of Optimal Tuning for PID Parameters in an AVR System

Slime Mould Algorithm Training Neural Network in Automatic Voltage Regulator

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

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