Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : NARMA

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.

Speed Control of Permanent Magnet D.C. Motor Using Neural Network Control

Lina J. Rashad

Engineering and Technology Journal, 2010, Volume 28, Issue 19, Pages 5844-5856

This paper proposes the speed control of a permanent magnet direct current
(PMDC) motor by varying armature voltage. The objective is to control the
rotor angular speed to follow the desired value. The main feature of the
proposed controller is neural network, which captures the nonlinearity system of
the motor. Neural network (NN) performance is compared with the
conventional controller performance like PI (Proportional-Integral) controller to
show that NN performance is excellent. Numerous work reported in recent past
have shown that Artificial Neural Network (ANN) controller has a potential to
replace the conventional PI controller. Artificial Neural Network control
apparently offers a possibility of obtaining an improvement in the quality of the
speed response, compared to PI control. This research proposes NARMA-L2
(Nonlinear Autoregressive-Moving Average) as an improved ANNtechnique,
and trained as a close loop controller, which gives an ideal performance as
compared with PI controller to control the angular speed of rotor in a permanent
magnet dc (PMDC) motor. Simulation results show the effectiveness of the
proposed control scheme.The entire system has been modeled using MATLAB

Artificial Neural Control of 3-Phase Induction Motor Slip Regulation Using SPWM Voltage Source Inverter

Lina J. Rashad; Fadhil A. Hassan

Engineering and Technology Journal, 2010, Volume 28, Issue 12, Pages 2392-2404

Variable-Voltage Variable-Frequency control represents the most
successful used method in speed control of 3-phase induction motor, which is
implemented by using PWM techniques. This paper proposes modeling and
simulation of sinusoidal PWM voltage source inverter as a VVVF A.C drive. The
dynamic model, simulation of 3-phase induction motor, and open loop speed
control system is proposed too. The PI closed loop controller of rotor slip
regulation is illustrated as a traditional speed control method, which gives stable
operation behavior of motor speed in the constant torque region with settling time
=0.5 sec and maximum overshot =20%, but unstable operation in the field
weakening regions with steady state error =15%. The Artificial Neural Network
(ANN) is going to be the modern type of speed controller. This paper proposes
NARMA-L2 (Nonlinear Autoregressive-Moving Average) neural network as an
improved Artificial Neural Network technique, and trained as a close loop slip
regulation controller, which gives an ideal performance with settling and rise time
= 0.18 sec, maximum overshot and steady state error less than 1% in different
speed range and constant air gap flux, including the field weakening regions.