Jamal Abd Ali

Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling

This paper aims to determine the distance between the mobile sensor node (i.e., bicycle) and the anchor node (i.e., coach) in outdoor and indoor environments. Two approaches were considered to estimate such a distance. The first approach was based on the traditional channel propagation model that used the log-normal shadowing model (LNSM), while the second approach was based on a proposed hybrid particle swarm optimization-artificial neural network (PSO-ANN) algorithm to improve the distance estimation accuracy of the mobile node. The first method estimated the distance according to the LNSM and the measured received signal strength indicator (RSSI) of the anchor node, which in turn used the ZigBee wireless protocol. The LNSM parameters were measured based on the RSSI measurements in both outdoor and indoor environments. A feed-forward neural network type and the Levenberg-Marquardt training algorithm were used to estimate the distance between the mobile node and the coach. The hybrid PSO-ANN algorithm significantly improved the distance estimation accuracy more than the traditional LNSM method without additional components. The hybrid PSO-ANN algorithm achieved a mean absolute error of 0.022 and 0.208 m for outdoor and indoor environments, respectively. The effect of anchor node density on localization accuracy was also investigated in the indoor environment.

An improved fuzzy logic controller design for PV inverters utilizing differential search optimization

This paper presents an adaptive fuzzy logic controller (FLC) design technique for photovoltaic (PV) inverters using differential search algorithm (DSA). This technique avoids the exhaustive traditional trial and error procedure in obtaining membership functions (MFs) used in conventional FLCs. This technique is implemented during the inverter design phase by generating adaptive MFs based on the evaluation results of the objective function formulated by the DSA. In this work, the mean square error (MSE) of the inverter output voltage is used as an objective function. The DSA optimizes the MFs such that the inverter provides the lowest MSE for output voltage and improves the performance of the PV inverter output in terms of amplitude and frequency. The design procedure and accuracy of the optimum FLC are illustrated and investigated using simulations conducted for a 3 kW three-phase inverter in a MATLAB/Simulink environment. Results show that the proposed controller can successfully obtain the desired output when different linear and nonlinear loads are connected to the system. Furthermore, the inverter has reasonably low steady state error and fast response to reference variation.

A Random Forest Regression Based Space Vector PWM Inverter Controller for the Induction Motor Drive

This paper presents a random forest (RF) regression based implementation of space vector pulse width modulation (SVPWM) for a two-level inverter to improve the performance of the three-phase induction motor (TIM) drive. The RF scheme offers the advantage of rapid implementation and improved prediction for the SVPWM algorithm to improve the performance of a conventional space vector modulation scheme. In order to show the superiority of the proposed RF technique to other techniques, an adaptive neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) based SVPWM schemes are also used and compared. The proposed speed controller uses a backtracking search algorithm to search for the best values for the proportional-integral controller parameters. The robustness of the RF-based SVPWM is found superior to the ANFIS and ANN controllers in all tested cases in terms of damping capability, settling time, steady-state error, and transient response under different operating conditions. The prototype of the optimal RF-based SVPWM inverter controller of induction motor drive is fabricated and tested. Several experimental results show that there is a good agreement of the speed response and stator current with the simulation results which are verified and validated the performance of the proposed RF-based SVPWM inverter controller.

Rule-based Fuzzy and V/f Control for Induction Motor Speed Responses Using SVPWM Switching Technique

This paper describes the development of a three-phase induction motor (TIM) drive speed controller. A rule-based fuzzy logic controller (FLC) is developed for TIM speed control in non-linear systems. Speed control applications are tested by conducting simulations under different operating conditions. To achieve reliable TIM operation, the space vector pulse width modulation (SVPWM) scheme is used to generate gate signals for the three-phase, two-level inverter. The SVPWM technique demonstrates excellent performance in TIM speed control. The scalar control (V/f control), which is inexpensive, simple to implement in hardware, and applicable to medium- and high-speed rated TIM applications, is used to control the developed TIM. Results show that the implementation of rule-based fuzzy with V/f control and the SVPWM technique for TIM speed control provides superior performance, which is sufficiently robust and intelligent for real-time applications. Streszczenie. Opisano nową metode sterowania predkością trojfazowego silnika indukcyjnego. Wykorzystano sterownik bazujący na logice rozmytej umozliwiający sterowanie w systemach nieliniowych. Do bramkowania sygnalu dwupoziomowego przeksztaltnika wykorzystano wektorowa modulacje szerokości impulsu SVPWM. Zastosowano tez skalarny przetwornik V/f. Fuzzy logic sterownik wykorzystujący modulacje SVPWM do kontroli szybkości silnika indukcyjnego

Artificial neural network based controller for home energy management considering demand response events

Electricity demand response and residential load modeling play important roles in the development of home energy management system. Accurate load models are required to produce a load profile at residential level. In this paper, modeling of four load types that include air conditioner, electric water heater, washing machine, and refrigerator are developed considering customer lifestyle and priority by using Matlab/ Simulink. In addition, the home energy management controller is proposed using artificial neural network (ANN) to predict the optimal ON/OFF status of the home appliances. The feedforward neural network type and Levenberg-Marquardt (LM) training algorithm are chosen for training the ANN in the Matlab toolbox. Results showed that the proposed ANN based controller can decrease the energy consumption for home appliances at specific time and can maintain the total household power consumption below its demand limit without affecting customer lifestyles.

Rule Base Home Energy Management System Considering Residential Demand Response Application

The increasing number of consumer and household appliances causes the rise in home energy use. Therefore, home energy management (HEM) technology is essential to manage and reduce electricity consumption. The objective of this paper is to present an intelligent algorithm for HEM using rule base technique to manage the power consumption with demand response (DR) feature. The scheduling algorithm considers household loads according to the comfort level, customer preference setting and priority of appliance that can be managed at a given time. The algorithm guarantees the total power consumption to be below the electrical demand limit. To exhibit the performance of the proposed HEM, a number of simulations are carried out including DR signal from the network operator. The results show that the algorithm can effectively respond to DR signal, comfort level, customer preference setting and priority of appliance. Furthermore, the algorithm is simple to implement and has flexibility to control the appliances.

Gravitational search algorithm based tuning of a PI speed controller for an induction motor drive

Proportional-integral (PI)-controller is very useful for controlling speed and mechanical load variables for the three-phase induction motor (TIM) operation. However, the conventional PI-controller has a very exhaustive trial and error procedure for obtaining it is parameters. In this paper, PI speed controller has been improved in it is design technique to suite TIM by utilizing a gravitational search algorithm (GSA) optimization technique. The mean absolute error (MAE) of the speed response has been used as an objective function. An optimal GSA based PI speed controller (GSA-PI) objective function is also employed to tune and minimize the MAE for developing the performance of the TIM in terms of changes speed and mechanical load. This experiment use space vector pulse width modulation (SVPWM) technique to create pulse width modulation for switching devices for three phase bridge inverter. Results obtained from the GSA-PI speed controller are compared with those obtained through particle swarm optimization (PSO) to validate the developed controller. Then it has been proved that the robustness of the GSA-PI speed controller is far better than that of the1 PSO controller in all tested cases in terms of damping capability and transient response under different mechanical loads and speeds.

A Quantum Lightning Search Algorithm-Based Fuzzy Speed Controller for Induction Motor Drive

This paper presents a quantum lightning search algorithm (QLSA) -based optimization technique for controlling speed of the induction motor (IM) drive. The developed QLSA is implemented in fuzzy logic controller to generate suitable input and output fuzzy membership function for IM drive speed controller. The main objective of this paper is to develop QLSA-based fuzzy (QLSAF) speed controller to minimise the mean absolute error in order to improve the performance of the IM drive with changes in speed and mechanical load. The QLSAF-based speed controller is implemented in simulation model in the MATLAB/Simulink environment and the prototype is fabricated and experimentally tested in a fully integrated DSP for controlling the IM drive system. The experimental results of the developed QLSAF speed controller are compared with the simulation results under different performance conditions. Several experimental results show that there are good agreement of the controller parameters, SVPWM signals, and different types of speed responses and stator currents with the simulation results, which are verified and validated the performance of the proposed QLSAF speed controller. Also, the proposed QLSAF speed controller outperforms other studies with settling time in simulation and in experimental implementation, which validates the controller performance as well.

Quantum-behaved lightning search algorithm to improve indirect field-oriented fuzzy-PI control for IM drive

The main objective of this study is to develop a quantum-behaved lightening search algorithm (QLSA) to improve the indirect field-oriented fuzzy-proportional-integral (PI) controller technique to control a three-phase induction motor (TIM) drive. The generated adaptive PI current control parameters and fuzzy membership functions are carried to design induction motor drive speed controller to minimize the fitness function formulated by QLSA. An optimal QLSA-based indirect field-oriented control (QLSA-IFOC) fitness function is used to reduce the mean absolute error of the rotor speed to improve the performance of the TIM with varying speed and mechanical load. Results obtained from the QLSA-IFOC are compared with those obtained through lightening search algorithm, gravitational search algorithm, backtracking search algorithm, and particle swarm optimization to validate the developed controller. The optimization results of objective functions in terms of box plots and iterations show that the QLSA algorithm outperforms the other optimization algorithms. Moreover, the QLSA-IFOC controller performed well in all tests in terms of transient response. The developed controller also minimizes overshoot, increases damping capability, and reduces the root-mean-square error, as well as standard deviation under sudden change of speed and mechanical loads. A comparative analysis is performed between simulation and experimental results to justify the efficiency of the developed controller.

Optimized speed controller for induction motor drive using quantum lightning search algorithm

This paper presents an improve proportional-integral-derivative (PID) controller design technique for controlling a three-phase induction motor (TIM) speed drive using quantum lightning search algorithm (QLSA). This proposed controller avoids the exhaustive conventional trial- and-error procedure for obtaining PID parameters. Objective function using in the proposed controller is mean absolute error (MAE) to enhance the TIM speed performance under sudden change of the speed and load conditions. The QLSA is used to improve two controller system PID and PI controllers in the TIM drive. Moreover, the QLSA algorithm comperes with three optimization algorithms, namely, lightning search algorithm (LSA), the backtracking search algorithm (BSA), the particle swarm optimization (PSO). Designed and validated the simulation model by using a MATLAB/Simulink environment. Results show that the QLSA-based PID and PI speed controller is achieved better results than the other optimization controllers through reduce of damping capability, enhance the transient response, minimize the MAE, root mean square error (RMSE) and standard division (SD) of the speed response.