Analysis and Simulation of the AVR System and Parameters Variation Effects

Faiz, Jawad; Shahgholian, Ghazanfar; Arezoomand, M.

doi:10.1109/powereng.2007.4380101

Cited by 29 publications

(9 citation statements)

References 6 publications

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“…In general, the mechanical and electrical measured parameters used to identify the model of steam power plant are translated into suitable transfer functions for various components in the power plant [1]. The model selected in this investigation is the turbine speed control system presented in a two-area system i.e.…”

Section: A Overviewmentioning

confidence: 99%

Performance Evaluation of Neuro Fuzzy Clustering for Hydro Thermal Power System

Maurya*,

Banerjee,

Kumar

2019

IJEAT

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To meet increasing power demands and reduce carbon emission additional renewable sources of power are added to existing thermal unit. Even network frequency change by 1% due to change in speed, may result in loss of synchronization with the rest of power system and finally resulting in power system black-out. Inter-connection of hydro units to existing thermal system can allow to transmission system to operate at full capacity. The paper investigates LFC by adding non-linearity’s to the thermal and hydro-thermal systems. Further PID and Neuro-Fuzzy Controllers are compared for these systems . A multi section steam turbine with re-heater was used for a single area system modeled in MATLAB, later two single area systems were combined to create a two area system, and its dynamics are studied by creating load perturbations. Form simulation studies it is shown that the proposed Neuro-Fuzzy controller was able to attain a setting time of 10 Sec which is comparatively lower than other existing speed controllers. The time domain response result of hydropower system proves that it provides more rapid output response and minimal overshoot.

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Section: A Overviewmentioning

confidence: 99%

Performance Evaluation of Neuro Fuzzy Clustering for Hydro Thermal Power System

Maurya*,

Banerjee,

Kumar

2019

IJEAT

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“…For mathematical modeling and transfer function of the four components, these components must be linearized, which takes into account the major time constant and ignores the saturation or other nonlinearities. The reasonable transfer function of these components may be represented, respectively, as follows [14][15][16][17][18][19]].…”

Section: B) Model Of An Avr Systemmentioning

confidence: 99%

Adaptive Neuro Fuzzy Inference System PID Controller for AVR System Using SNR-PSO Optimization

Yavarian¹,

Mohammadian²,

Hashemi³

2015

ijeei

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This paper presents an intelligent Proportional Integral Derivative (PID) controller for Automatic Voltage Regulator (AVR) system using Adaptive Neuro Fuzzy Inference System (ANFIS). In the proposed method, the PID controller parameters are tuned off line by using combination of Signal to Noise Ratio (SNR) and Particle Swarm Optimization (PSO) algorithm to minimize the cost function over a wide range of operating condition. The optimal values of PID controller parameters obtained from SNR-PSO algorithm for each considered operating condition are used to train ANFIS. Therefore, the proposed techniques could online tune the PID controller parameters at any other operating condition to improve the transient response of the system. In order to evaluate the performance of the SNR-PSO PID controller, the results are compared with the Genetic Algorithm (GA). Also, the performance of proposed intelligent PID controller is compared with the robust SNR-PSO PID controller with fixed parameters. The comparison shows the SNR-PSO based ANFIS controller is more efficiency than robust PID controller.

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“…In this presented work, three area hydro-thermal reheat interconnected power system have been developed by using PI and FUZZY LOGIC controllers to illustrate the performance of load frequency control using MATLAB/SIMULINK software [10]. Several types of simulink models for different cases are developed with those controllers to obtain better dynamic behavior [2]. Frequency deviation plots for different cases are obtained as shown in figures.…”

Section: B) Fuzzy Logic Controllersmentioning

confidence: 99%

Effect of load variations on AGC of an interconnected hydro-thermal power system in presence of conventional PI and FL controllers

Kalyan¹,

Kumar²,

Prasad³

2014

2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies

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This Paper discusses the effects of load parameters and their variations on power system frequency. In this study an interconnected three area hydro-thermal power system with conventional PI controller and fuzzy logic controller is selected. From the mathematical modeling of power system, frequency is a function of power system parameters and for small changes in these parameters leads to small deviations in the system frequency. In case of extreme ends of parameters, even for small changes in load power there will be a chance of instability in overall power system. This paper deals both stable and unstable cases for different parameter variations. Simulations are carried out in MATLAB/SIMULINK software.

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Analysis and Simulation of the AVR System and Parameters Variation Effects

Cited by 29 publications

References 6 publications

Performance Evaluation of Neuro Fuzzy Clustering for Hydro Thermal Power System

Performance Evaluation of Neuro Fuzzy Clustering for Hydro Thermal Power System

Adaptive Neuro Fuzzy Inference System PID Controller for AVR System Using SNR-PSO Optimization

Effect of load variations on AGC of an interconnected hydro-thermal power system in presence of conventional PI and FL controllers

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