Mohamad Fani Sulaima

Optimal PID controller parameters for nonlinear gantry crane system via MOPSO technique

This paper presents development of an optimal PID controller for controlling the nonlinear gantry crane system. The PSO with linear weight summation approach is implemented for finding optimal PID parameters. The effectiveness of variation weight summation is observed in order to find the optimal performances of system. The system dynamic model is derived using Lagrange equation. A combination of five parameters (PID and PD) controllers are utilized for positioning and oscillation control of the system. System responses including trolley displacement and payload oscillation are observed and analyzed. Simulation is conducted within Matlab environment to verify the performance of the controller. It is demonstrated that implementation of linear weight summation approach in controlling system is useful in order to find the required performances according to the needs and circumstances.

A Distribution Network Reconfiguration based on PSO: Considering DGs sizing and allocation evaluation for voltage profile improvement

The optimized network reconfiguration and Distributed Generations (DG) sizing with allocation instantaneously via Particle Swarm Optimization (PSO) proposed a new way of allocation DG based on low voltage profile. This method consists of three steps. It started with categorized the switching sequences for radial network configuration while observe the P losses and the profile of voltage without DG. The second step is reconfiguration feeder for reduce losses via DGs allocation based on substations geographical location. The final step is sizing and allocation DGs at each bus with low voltage profile produced from the first step, used to mend the voltage profile and minimize the Plosses also compared the result with the geographical based allocation results. The objective of this study is to mend the voltage profile while decreasing the Plosses by using optimization technique considering network reconfiguration, DGs Sizing and allocation concurrently. Four cases are compared which is case 1 is the initial case and taken as a reference. All three stages are tested on standards IEEE 33 bus system by using Particle Swarm Optimization (PSO) technique in MATLAB software. This method proved that improvement of Plosses and voltage profile has been made by change of the switching topology with DGs sizing and allocation technique respectively.

JOINT TORQUE ESTIMATION MODEL OF SEMG SIGNAL FOR ARM REHABILITATION DEVICE USING ARTIFICIAL NEURAL NETWORK TECHNIQUES

Rehabilitation device is used as an exoskeleton for peoples who had failure of their limb. Arm rehabilitation device may help the rehab program to who suffered with arm disability. The device is used to facilitate the tasks of the program and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. To minimize the used of mental forced for disable patients, the rehabilitation device can be utilize by analyzing the surface EMG signal of normal people that can be implemented to the device. The objective of this work is to model the muscle EMG signal to torque for a motor control of the arm rehabilitation device using Artificial Neural Network (ANN) technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN) to model the EMG signal to torque value. The performance result of the network is measured based on the Mean Squared Error (MSE) of the training data and Regression (R) between the target outputs and the network outputs. The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control.

A DNR and DG Sizing Simultaneously by Using EPSO

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an efficient hybridization of both Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) methods which is called the Evolutionary Particle Swarm Optimization (EPSO). The proposed method is used to find the optimal network reconfiguration and optimal size of Distribution Generation (DG) simultaneously. The main objective of this paper is to gain the lowest result of real power losses in the distribution network while improve the computational time as well as satisfying other operating constraints. A comprehensive performance analysis is carried out on IEEE 33 bus distribution system. The proposed method is applied and its impact on the network reconfiguration for real power loss is investigated. The results are presented and compared with the strategy of separated DG sizing and network reconfiguration.

Predicting EMG Based Elbow Joint Torque Model Using Multiple Input ANN Neurons for Arm Rehabilitation

This paper illustrates the Artificial Neural Network (ANN) technique to predict the joint torque estimation model for arm rehabilitation device in a clear manner. This device acts as an exoskeleton for people who had failure of their limb. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. The objective of this work is to model the muscle EMG signal to torque using ANN technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN). The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control.

DG sizing and DNR based on REPSO for power losses reduction

The detrimental of losses in network can be solved by using Distribution Network Reconfiguration (DNR) and sizing the Distribution Generation (DG) concurrently. In determining the optimal sizing of DG and identifying the switching operation plan for network reconfiguration, an optimization method which is called as Rank Evolutionary Particle Swarm (REPSO) will be introduced. The main objectives of this paper are to minimize the total power losses in a radial distribution network and to find the most accurate and acceptable size of DG. A comprehensive performance analysis will be carried out on IEEE-33 bus system to show the effectiveness of the REPSO over conventional PSO and hybridization EPSO method. The reliability of proposed method will hope to help the power system engineer in reducing the distribution feeder losses and improve system security in the future.

Features extraction of EMG signal using time domain analysis for arm rehabilitation device

Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program whom suffers from arm disability. The device that is used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity, the force of movements should minimize the mental efforts. Therefore, the rehabilitation device should analyze the surface EMG signal of normal people that can be implemented to the device. The signal is collected according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The EMG signal is implemented to set the movements’ pattern of the arm rehabilitation device. The filtered EMG signal was extracted for features of Standard Deviation (STD), Mean Absolute Value (MAV) and Root Mean Square (RMS) in time-domain. The extraction of EMG data is important to have the reduced vector in the signal features with less of error. In order to determine the best features for any movements, several trials of extraction methods are used by determining the features with less of errors. The accurate features can be use for future works of rehabilitation control in real-time.Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program whom suffers from arm disability. The device that is used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity, the force of movements should minimize the mental efforts. Therefore, the rehabilitation device should analyze the surface EMG signal of normal people that can be implemented to the device. The signal is collected according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The EMG signal is implemented to set the movements’ pattern of the arm rehabilita...

A Comprehensive Study of Improved Evolutionary Particle Swarm Optimization (IEPSO) for Network Reconfiguration with DGs Sizing Concurrently

This paper deals with the reconfiguration of the distribution network system to investigate the total power losses considering Distribution Generations (DGs) sizing concurrently. To overcome other limitations and enhance the solution performances, a new optimization approach called Improved Evolutionary Particle Swarm Optimization (IEPSO) is proposed. The primary aim of this study is to investigate the contribution of the proposed algorithms towards total power losses by considering the optimum DG size simultaneously. The proposed method is compared with the traditional Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) respectively. The amount of time that an algorithm spends in obtaining an alternative topological status for the system power loss reduction and distribution generation sizing is taken into consideration. In this context, the study is tested using IEEE 33 bus distribution system.

Optimal Placement and Sizing of Renewable Distributed Generation via Gravitational Search Algorithm

The installation of distributed generation (DG) gives advantages to the environment such as, it contribute in the reduction of non-peak operating cost, diversification of energy resources, lower losses thus improving overall organization. These advantages might be rescinded if no proper location and sizing of DGs are considered before the DG’s installation. This paper offers an optimal location and sizing of multiple DGs using heuristic method called gravitational search algorithm (GSA). The suggested algorithm is tested on 13-bus radial distribution system. This method is being compared with particle swarm optimization (PSO) in terms of system power loss, voltage deviation and total voltage harmonic distortion (THDv). GSA shows the ability to locate and sized DG optimally with a better performance and more reliable than PSO.

A DNR by Using Rank Evolutionary Particle Swarm Optimization for Power Loss Minimization

Distribution Network Reconfiguration (DNR) is required to identify the best topology network in order to fulfill the power demand with minimum power losses. This paper proposes a new method which is called as Rank Evolutionary Particle Swarm Optimization (REPSO). The proposed method is a combination of the Particle Swarm Optimization (PSO) and the traditional Evolutionary Programming (EP) algorithm with a rejuvenation of the additional of ranking element. The main objective of this paper is to reduce the power losses while improving the convergence time. The proposed method will be implemented and the real power losses in the IEEE 33-bus test system will be investigated and analyzed accordingly. The results are compared to the conventional PSO and hybridization EPSO method and it is hoped to help the power system engineer in securing the network with the less power loss in the future.