Nor Rul Hasma Abdullah

Thyristor Controlled Series Compensator planning using Evolutionary Programming for transmission loss minimization for system under contingencies

The increment of power demand can cause a rapid reduction in voltage profile that can disrupt system stability leading to potential failure on the entire system as the system has to work under a stressed condition. Thus, the Flexible AC Transmission System (FACTS) are integrated in power system to control the power flow in specific lines and improve the security of transmission line. This paper proposes a static voltage stability index (SVSI) for placement of Thyristor Controlled Series Compensator (TCSC) and used Evolutionary Programming (EP) techniques for optimal sizing of TCSC used for system under contingencies. The objective of the study is to minimize the total real power losses, and at the same time monitoring the voltage profile along with the installation cost. Critical contingency cases have been used to decide optimal sizing for the placement of TCSC. The TCSC devices are installed in the system in order to enhance the system security; performed on the IEEE 30-bus RTS for several loading conditions. The simulations results are compared with those obtained from the Artificial Immune System (AIS) technique in the attempt to highlight its merit.

Computational intelligence technique for solving power scheduling optimization problem

Computational Intelligence technique has become a prominent technique in solving engineering optimization problem. One of the problems which can be addressed in this issue is problems related to power system optimization. This paper presents computational intelligence technique for solving power scheduling optimization problem. Evolutionary Programming technique has been applied to minimise total transmission loss; considering the scheduling of active power and controlling the reactive power as the main control variables. In this study, the control process of reactive power and scheduling of active power at all generators will help control the power flow in the system. This has given impact to the current flow which influences the total transmission loss. Validation through a reliability test system revealed its superiority. Verification performed through comparative studies with other optimization technique revealed that the proposed computational intelligence technique produced promising results.

Constrained Active Power Reschedule Using Immune System Technique for Static Security Enhancement

This paper presents a constrained active power reschedule using immune system for static security enhancement. The study aims to evaluate the static security when a power transmission system is subjected to multi-contingencies. Voltage stability improvement is achieved by performing active power rescheduling in which the optimal value is determined using artificial immune system (AIS). The method was test on IEEE 30-Bus RTS system and results have been compared with Evolutionary Programming (EP) indicating that AIS outperformed EP.

Constrained Reactive Power Control Using Evolutionary Computation Technique for Static Security Enhancement

This paper presents Evolutionary Computing technique for solving constrained reactive power control (CRPC) problem in the attempt to enhance voltage stability under contingencies, while minimizing transmission loss and maintaining voltage level at an acceptable level. In this study, evolutionary programming (EP) was chosen as the Evolutionary Computing (EC) technique for solving the CRPC; taking into consideration two separate objective functions. Static voltage stability enhancement and minimization of real power loss are implemented separately on a reliability test system. Comparative studies performed with respect to Artificial Immune System (AIS) have highlighted that EP outperformed AIS for both objective functions.

Computer vision-based hand deviation exercise for rehabilitation

Computerized monitoring of the home based rehabilitation exercise has many benefits and it has attracted considerable interest among the computer vision community. Nowadays, many rehabilitation systems are proposed, most of the targeted disability is for stroke patient. Some of patient or user just wants to take certain part for rehabilitation. Therefore, this paper is focusing on hand rehabilitation system. The importance of the rehabilitation system is to implement the specific exercise for the specific requirements of the patients that needs rehabilitation therapy. This paper presents the specific hand rehabilitation system using computer vision method. The specific hand rehabilitation implemented in this system is a hand deviation exercise. This exercise is benefited to improve the mobility of the hand and reduce the pain. The hand tracking and finger detection method are used in this hand rehabilitation system. The result of the exercise can be used as a training data for the analysis of the injured hand recovery and healing process.

Finger application using K-Curvature method and Kinect sensor in real-time

Gesture is one of the important aspects of human interaction and also in the context of human computer interaction. Gesture recognition is the mathematical interpretation of a human motion by a computing device. It is often used hand gestures for input commands in personal computers. By recognizing the hand gesture as input, it allows the user to access the computer interactively and makes interaction more natural. This paper presents a finger detection application by using Kinect. Kinect is a depth sensor that is an effective device to capture the gesture in real-time. To detect and recognize the fingertips, it needs to extract the detail of the captured hand image using image processing methods. In this paper, the proposed method is to detect and recognize the fingertips by using the K-Curvature algorithm. Finally, the finger counting application is applied and the proposed method is discussed at the end of this paper. The results obtained from the experiment show that the acceptable average accuracy for the fingertips detection is 73.7% and the average processing time is 15.73 ms. By considering this result, the application of the proposed method can be extended to the hand rehabilitation system.

Waypoint Navigation of Quad-rotor MAV Using Fuzzy-PID Control

Quad-rotor Micro Aerial Vehicle (MAV) is a multi-rotor MAV with four propellers which propel the MAV up to the air and move around. It has high maneuverability to move around, such as roll, pitch and yaw movements. However, line of sight and radio control effective range are the major limitation for the MAVs which significantly shorten the travel distance. Therefore, we proposed a waypoint navigation quad-rotor MAV based on Fuzzy-PID controller in this paper. User can set mission with multiple waypoint and the Fuzzy-PID controller will control MAV autonomously moving along the waypoint to the desired position without remotely controlled by radio control and guidance of pilot. The results show Fuzzy-PID controller is capable to control MAV to move to the desired position with high accuracy. As the conclusion, Fuzzy-PID controller is successfully designed for waypoint navigation in quad-rotor MAV. The result shows that the overshoot percentage (%OS) of the designed Fuzzy-PID controller for x position is 2.17% while y position is 0.93%. Additionally, the steady-state error for x position and y position are 0.54% and 0.56% respectively. Therefore, the performance of Fuzzy-PID controller is better than PID controller.

Multi-objective evolutionary programming for static var compensator (SVC) in power system considering contingencies (N-m)

Efficiency, reliability, high power quality and continuous operation are important aspects in electric vehicle attraction system. Therefore, quick fault detection, isolation and enhanced fault-tolerant control for open-switches faults in inverter driving systems become more and more required in this filed. However, fault detection and localization algorithms have been known to have many performance limitations due to speed variations such as wrong decision making of fault occurrence. Those weaknesses are investigated and solved in this paper using currents magnitudes fault indices, current direct component fault indices and a decision system. A simulation model and experimental setup are utilized to validate the proposed concept. Many simulation and experimental results are carried out to show the effectiveness of the proposed fault detection approach.The inverter with critical loads should be able to provide critical loads with a stable and seamless voltage during control mode change as well as clearing time. The indirect current control has been proposed for providing stable voltage with critical load during clearing time and seamless control mode transfer of inverters. However, the islanding detection is difficult since with the indirect current control the magnitude and frequency of voltage do not change when the islanding occurs. The conventional anti-islanding method based on the magnitude and frequency of voltage variation cannot apply to the indirect current control. This paper proposes an islanding detection method for the indirect current control. The proposed islanding detection method can detect the islanding using reactive power perturbation and observation when the frequency and magnitude of voltage don’t vary during clearing time. In order to verify the proposed anti-islanding method, the experimental results of a 600W three-phase inverter are provided.

Correlation of Objective Assessment of Facial Paralysis with House-Brackmann Score

This article illustrated a brief review of some objective methods in assessing facial nerve function for facial nerve paralysis which were correlated with House-Brackmann Grading System (HBGS). A rigorous search of online databases such as Springer, Elsevier and IEEE was conducted from June, 2015 to November, 2016 to discover and analyze the previous works in facial nerve assessment methods for facial paralysis. Several domains such as facial grading system and methods used to evaluate the facial nerve function were extracted for further analysis. Different keywords were used to acquire the studies based on the desire criteria. A total of 8 articles were identified and were analyzed for inclusion in this search. In conclusion, this review has presented an initial overview for further improvements in objective facial nerve assessment which has to be correlated with subjective assessment to make it more reliable and useful in clinical practice.

Fault detection and isolation for complex system

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.