Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Automatic Voltage Regulator


Study the Robustness of Automatic Voltage Regulator for Synchronous Generator Based on Neuro-Fuzzy Network

Abdulrahim Thiab Humod; Yasir Thaier Haider

Engineering and Technology Journal, 2015, Volume 33, Issue 3, Pages 612-627

Modern power systems are complex and non-li¬near and their operating conditions can vary over a wide range, and since neuro - fuzzy networkcan be used as intelligent controllers to control non-li¬near dynamic systems through learning, which can easily accommodate the non-linearity, time dependencies, model uncertainty and external disturbances.ANeuro-Fuzzy model system is proposed as an effective neural network controller model to achieve the desired robust Automatic Voltage Regulator (AVR) for Synchronous Generator (SG) to maintain constant terminal voltage. TheconcernedNeuro-fuzzy controller for AVRis examined on different models of SG andloads. The results show that the Neuro-Fuzzy -controllers have excellent responses for all SG models and loads in the view point of transientresponse and system stability compared with optimal PID controllers tuned by practical swarm optimization.They also show that the margins of robustness for Neuro-Fuzzy -controller aregreater thanPID controller.

Fuzzy-Swarm Controller for Automatic Voltage Regulator of Synchronous Generator

Abdulrahim Thiab Humod; Wisam Najm Al-Din Abed

Engineering and Technology Journal, 2012, Volume 30, Issue 3, Pages 454-473

The main objective of this work is to propose Artificial Intelligence (AI) controller
to enhance the performance of Automatic Voltage Regulator (AVR) of
a Synchronous Generator (SG) during different loading conditions. The proposed
mathematical model of the SG with saturation nonlinearities is connected to different
loads in two ways. The first each load is connected individually and the second the
SG loads change during the operation to ensure the robustness of controller for wide
load variations. Two types of controllers are used. The first controller is the
Proportional-Integral (PI) based on Particle Swarm Optimization (PSO) technique to
obtain optimal gains. The second controller is Fuzzy PD+I with gains and
Membership Functions (MFs) tuned by PSO technique. The results show the
improvement of PI-PSO performance on conventional PI controller; also show the
improvement in the performance of Fuzzy PD+I using PSO technique on PI-PSO.
The simulation of SG is performed using MATLAB program version 7.10.0.499
(R2010a).

Neural Network-Based Robust Automatic Voltage Regulator (AVR) of Synchronous Generator

Abdullah Sahib Abdulsada; Abdulrahim Thiab Humod

Engineering and Technology Journal, 2011, Volume 29, Issue 7, Pages 1372-1385

The voltage stability and power quality of the electrical system depend on proper operation of AVR. Nowadays, Design technology of AVR is being broadly improved.
Nonlinearities and parametric uncertainties are unavoidable problem faced in
controlling the output voltage of Synchronous Generator (SG) when working alone or with others. This paper proposes a Nonlinear Auto Regressive-Moving Average control (NARMA-L2) as a voltage controller which is one type of Neural Network (NN) plant structure. Nonlinearities due to the effect of saturation in machine between generated voltage and field current, uncertainties arise because variation of the load connected with time and the change of rotors resistance with temperature. Due to this fact, Proportional- Integral- Derivative (PID) controller cannot be used effectively since it is developed based on linear system theory. NN controller shows less over shoot and settling time than PID controller with different conditions of load. Also NN controller shows high robust characteristic than PID controller.