Kay Soon Low

A Global Maximum Power Point Tracking Scheme Employing DIRECT Search Algorithm for Photovoltaic Systems

This paper presents a maximum power point tracking approach for a photovoltaic system using the dividing rectangles algorithm. The new approach overcomes some weaknesses of the existing methods such as the perturb and observe method as it is capable of searching for global maximum. This is particularly important for a system that is partially shaded. To validate the performance of the proposed scheme, experimental studies have been conducted. The results have shown that the proposed approach is robust and possesses a fast tracking speed.

Photovoltaic Model Identification Using Particle Swarm Optimization With Inverse Barrier Constraint

The photovoltaic (PV) model is used in simulation studies to validate system design such as the maximum power point tracking algorithm and microgrid system. It is often difficult to simulate a PV module characteristic under different environmental conditions due to the limited information provided by the manufacturers. In this paper, a new approach using particle swarm optimization (PSO) with inverse barrier constraint is proposed to determine the unknown PV model parameters. The proposed method has been validated with three different PV technologies and the results show that the maximum mean modeling error at maximum power point is less than 0.02% for Pmp and 0.3% for Vmp.

Zero-phase odd-harmonic repetitive controller for a single-phase PWM inverter

In this paper, a zero-phase odd-harmonic repetitive control scheme is proposed for pulse-width modulation inverters. The proposed repetitive controller combines an odd-harmonic periodic generator with a noncasual zero-phase compensation filter. It occupies less data memory than a conventional repetitive controller does. Moreover, it offers faster convergence of the tracking error, and yields very low total harmonics distortion (THD) and low tracking error. Analysis and design of the proposed system are presented. Experimental results with the proposed repetitive controller are presented to validate the approach. The phenomena of even-harmonic residues in the proposed control system is discussed and experimentally demonstrated.

Cooperative Communications in Ultra-Wideband Wireless Body Area Networks: Channel Modeling and System Diversity Analysis

In this paper, we explore the application of cooperative communications in ultra-wideband (UWB) wireless body area networks (BANs), where a group of on-body devices may collaborate together to communicate with other groups of on-body equipment. Firstly, time-domain UWB channel measurements are presented to characterize the body-centric multipath channel and to facilitate the diversity analysis in a cooperative BAN (CoBAN). We focus on the system deployment scenario when the human subject is in the sitting posture. Important channel parameters such as the pathloss, power variation, power delay profile (PDP), and effective received power (ERP) crosscorrelation are investigated and statistically analyzed. Provided with the model preliminaries, a detailed analysis on the diversity level in a CoBAN is provided. Specifically, an intuitive measure is proposed to quantify the diversity gains in a single-hop cooperative network, which is defined as the number of independent multipaths that can be averaged over to detect symbols. As this measure provides the largest number of redundant copies of transmitted information through the body-centric channel, it can be used as a benchmark to access the performance bound of various diversity-based cooperative schemes in futuristic body sensor systems.

Optimizing the Localization of a Wireless Sensor Network in Real Time Based on a Low-Cost Microcontroller

In this paper, a low-cost microcontroller-based system that uses the pedometer measurement and communication ranging between neighboring nodes of a wireless sensor network for localization is presented. Unlike most of the existing methods that require good network connectivity, the proposed system works well in a sparse network. As the localization requires solving of nonlinear equations in real time, two optimization approaches, namely, the Gauss-Newton algorithm and the particle swarm optimization have been studied. The localization and optimization algorithms have been implemented with a microcontroller. The performance has been evaluated with experimental results.

Model Predictive Control of Parallel-Connected Inverters for Uninterruptible Power Supplies

Uninterruptible power supplies (UPSs) have been used in many installations for critical loads that cannot afford power failure or surge during operation. It is often difficult to upgrade the UPS system as the load grows over time. Due to lower cost and maintenance, as well as ease of increasing system capacity, the parallel operation of modularized small-power UPS has attracted much attention in recent years. In this paper, a new scheme for parallel operation of inverters is introduced. A multiple-input-multiple-output state-space model is developed to describe the parallel-connected inverters system, and a model-predictive-control scheme suitable for paralleled inverters control is proposed. In this algorithm, the control objectives of voltage tracking and current sharing are formulated using a weighted cost function. The effectiveness and the hot-swap capability of the proposed parallel-connected inverters system have been verified with experimental results.

A Repetitive Model Predictive Control Approach for Precision Tracking of a Linear Motion System

In this paper, a new model predictive control (MPC) approach suitable for high precision linear motion drive operating with repetitive tracking tasks is presented. For the proposed predictive controller, the feedforward controller of the conventional MPC has been modified to provide zero-phase learning property. This is achieved by augmenting the reference trajectory with a phase-compensated term that is updated with the historical tracking error. The proposed approach attempts to combine the merits of both the conventional MPC and repetitive control schemes. Experimental results have demonstrated that the system effectively reduces the tracking error from the periodic disturbance caused by the friction. Its performance under varying reference conditions and different loadings shows that the system is robust.

Unrestrained Measurement of Arm Motion Based on a Wearable Wireless Sensor Network

Techniques that could precisely monitor human motion are useful in applications such as rehabilitation, virtual reality, sports science, and surveillance. Most of the existing systems require wiring that restrains the natural movement. To overcome this limitation, a wearable wireless sensor network using accelerometers has been developed in this paper to determine the arm motion in the sagittal plane. The system provides unrestrained movements and improves its usability. The lightweight and compact size of the developed sensor node makes its attachment to the limb easy. Experimental results have shown that the system has good accuracy and response rate when compared with a goniometer.

Multichannel Pulse-Coupled-Neural-Network-Based Color Image Segmentation for Object Detection

This paper proposes a pulse-coupled neural network (PCNN) with multichannel (MPCNN) linking and feeding fields for color image segmentation. Different from the conventional PCNN, pulse-based radial basis function units are introduced into the model neurons of PCNN to determine the fast links among neurons with respect to their spectral feature vectors and spatial proximity. The computing of the color image segmentation can be implemented in parallel on a field-programmable-gate-array chip. Furthermore, the results of segmentations are applied to an object-detection scheme. Experimental results show that the performance of the proposed MPCNN is comparable to those of other popular image segmentation algorithms for the segmentation of noisy images while its parallel neural circuits improve the speed of processing drastically as compared with the sequential-code-based counterparts.

Opportunistic routing for body area network

Wireless body area network (WBAN) is an interesting application of sensor networks which can revolutionize our interaction with the outside world. WBAN like any other sensor network suffers limited energy resources and hence preserving the energy of the nodes is of great importance. Unlike typical sensor networks WBANs have few and dissimilar sensors. In addition, the body medium has its own propagation characteristic which means that the existing solution for preserving energy in wireless sensor networks might not be efficient in WBANs. The quality of the links between the nodes in WBAN is changing frequently due to the moving nature of the body. This can pose a major problem especially in the energy efficiency merit. In this work we have proposed an opportunistic scheme to exploit the body movements during the walking to increase the life time of the network. The results show that comparing to the existing methods this work can increase the life time of the network.