Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : PI controller


Effects of Evaporator and Condenser Temperatures on the Performance of a Chiller System With a Variable Speed Compressor

Ahmed H. Al-Hassani; Alaa R. Al-Badri

Engineering and Technology Journal, 2021, Volume 39, Issue 1, Pages 45-55
DOI: 10.30684/etj.2021.168090

The operation and performance of heat-pump systems are affected by indoor and outdoor operating conditions. Power consumption and system efficiency are related to evaporator and condenser working pressures. Intelligent controllers such as a proportional integral (PI) controller improve the performance of variable speed refrigeration systems (VSRs) with electronic expansion valve (EEV). Evaporator and condenser pressures affect the system power consumption and efficiency. In this study, the influence of evaporator and condenser temperatures on the performance of a variable speed refrigeration system with an EEV was experimentally investigated at constant cooling load. The experimental system comprises of a rotary compressor, shell-and-coil condenser, EEV, and shell-and-coil evaporator for one-ton cooling capacity with refrigerant R410. Compressor speed and EEV opening are controlled by a PI controller with two control loops and the refrigerant superheat (DS) is maintained at 7°C. The results show that at constant cooling capacity, the refrigerant flow rate rises with the increase in the compressor speed. The coefficient of performance (COP) is improved with low compressor speed. The System COP is increased by 3.3% with increasing evaporator inlet water temperature for 2°C due to the reduction in the compressor speed and compression ratio. High condenser inlet water temperature promotes the refrigerant subcooling.

Direct Torque Control of Induction Motor Based on Neurofuzzy

Abdulrahim T. Humod; Wiam I. Jabbar

Engineering and Technology Journal, 2013, Volume 31, Issue 17, Pages 3259-3273

The main objective of this work is to improve the speed and torque responses of
three phase Induction Motor (IM) during different loads and speeds conditions.
Induction Motor is most commonly used in different industrial applications, that
require fast dynamic response and accurate control over wide speed ranges.
Therefore, this work proposes Direct Torque Control (DTC). Particle Swarm
Optimization (PSO) technique is used for optimal gains tuning of PI. The results
show the improvement in the speed response of DTC, in terms of reducing steady
state error, ripple reduction in the torque and speed responses. Neurofuzzy
(ANFIS) controller is used to improve the performance of PI-PSO controller.
ANFIS controller is trained by using PI-PSO data. The results of the ANFIS
controller are better than PI-PSO in terms of torque ripple minimization, less
steady state error in the speed response and more robustness. The simulation of the
overall drive system is performed using MATLAB/Simulink program version 7.10
(R2010a).

Fuzzy-Swarm Controller for Speed-Governor of Synchronous Generator

Abdulrahim Thiab Humod; Wisam Najm Al-Din Abed

Engineering and Technology Journal, 2013, Volume 31, Issue 7, Pages 1239-1262

The main objective of this work is to propose an intelligent controller to enhance the performance of hydraulic turbine speed-governor of a Synchronous Generator (SG) during different loading conditions.
The proposed mathematical model of the SG is connected to different loads in two ways. First, each load is connected individually and 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.

An Adaptive Neuro-Fuzzy Inference System for Speed Control of Three-Phase Induction Motor

Lina J. Rashad

Engineering and Technology Journal, 2012, Volume 30, Issue 11, Pages 1897-1911

Conventional Proportional Integral (PI) controller of A.C drives are widely used in industry and many other applications, because of its simplicity, but it does not give high degree of speed control of induction motor. There are many types of controller: Proportional (P), Proportional Integral (PI), Proportional Integral Derivative (PID), and Intelligent controllers. The intelligent controller becomes a powerful tool for control nonlinear system in present time. This paper proposes the Adaptive Neuro-Fuzzy Inference System as an intelligent controller of the induction motor. In addition, the PI controller is presented in this paper as a conventional controller. The mathematical epresentation and simulation of the 3-phase induction motor is represented too. Also, a 3-phase voltage-fed Sinusoidal Pulse Width Modulation (SPWM) inverter is demonstrated and simulated. The overall system for both PI and ANFIS controllers are simulated using MATLAB/SIMULINK program. The comparison of simulation results between the conventional PI and the proposed
ANFIS performances shows that: the ANFIS controller gives superior performance than the conventional PI controller for wide range of speed variation.

Comparative Study of Temperature Control in a Heat Exchanger Process

Afraa H. Al-Tae; Safa A. Al-Naimi

Engineering and Technology Journal, 2012, Volume 30, Issue 10, Pages 1707-1731

In the present work the dynamic behavior of a plate heat exchanger (PHE)
(single pass counter current consists of 24 plates) studied experimentally and
theoretically to control the system. Different control strategies; conventional
feedback control, classical fuzzy logic control, artificial neural network (NARMAL2)
control and PID fuzzy logic control were implemented to control the outlet
cold water temperature. A step change was carried in the hot water flow rate which
was considered as a manipulated variable. The experimental heat transfer
measurements of the PHE showed that the overall heat transfer coefficient (U) is
related to the hot water flow rate (mh) by a correlation having the form:
U mh
0.7158 =11045
In this work the PHE model was found theoretically as a first order lead and
second order overdamped lag while the experimental PHE represented dynamically
(by PRC method) as a first order with negligible dead time value. A comparison
between the experimental and the theoretical model is carried out and good
agreement is obtained. The performance criteria used for different control modes
are the integral square error (ISE) and integral time-weighted absolute error (ITAE)
where the ITAE gave better performance. As well as the parameters of the step
performance of the system such as overshoot value, settling time and rise time are
used to evaluate the performance of different control strategies. The PID fuzzy
controller gave better control results of temperature rather than PI, PID and
artificial neural network controller since PID fuzzy controller combines the
advantages of a fuzzy logic controller and a PID controller. MATLAB program
version 7.10 was used as a tool of simulation for all the studies mentioned in this
work.

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).

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
toolbox.

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.