Yen Kheng Tan

Smart Personal Sensor Network Control for Energy Saving in DC Grid Powered LED Lighting System

Emerging smart grid technologies aim to renovate traditional power grid by integrating intelligent devices and their communications for monitoring and automation of the power grid to enable efficient demand-side energy management. In this paper, energy management in smart dc building grid powered dc electrical appliances like lighting is investigated, in particular energy savings from proposed personal lighting management strategy. Unlike conventional fluorescent lamps powered mainly by ac grid, LED luminaires are dc in nature, thus results in significant power conversion losses, if operate on traditional ac powered system, are analyzed with proposed dc distribution building grid for LED lighting. This paper continues to explore the use of smart wireless sensors for personal control of the dc grid powered networked LED lighting. Experimental results show that the proposed smart LED lighting system with an energy saving mechanism incorporated is able to achieve similar lighting performance as the conventional lighting condition, while at the same time, able to attain about 44% energy saving as compared to the original ac fluorescent system. For a low voltage dc grid being implemented, the maximum power loss and voltage drop of the developed dc distribution building grid are 2.25% and 3% respectively.

Autonomous Wearable Sensor Nodes With Flexible Energy Harvesting

Distributed wearable wireless sensors are widely employed in wireless body sensor network for various physiological monitoring purposes like health or performance related monitoring applications. The real challenges in employing these wearable wireless sensors on human subjects include: 1) bulky and rigid system design thus, it is difficult to conform to human body contour and 2) limited operational lifespan of batteries with finite energy supply. In this paper, an autonomous body-worn wireless sensor node with flexible energy harvesting (FEH) mechanism, able to conform to body contour, is proposed for biometric monitoring. To be totally sustainable and compact, the FEH mechanism is equipped with an ultralow power management circuit (PMC) specially designed on a flexible PCB. The flexible PMC is able to transfer near maximum electrical power from the input solar energy source to store in the supercapacitor for powering the wireless sensor node. The power consumption of the flexible PMC is only 32.86 μW. Experimental results show that under indoor condition, typical average lighting intensity of 320 lux, the wearable sensor node is able to continuously monitor the temperature of the wearer, read, and transmit back to the base node in a wireless manner, without the need of any battery. In addition, the designed FEH sensor node flexed onto the wearer body contour at an angle of 30° generates 56 μW of electrical power, sufficient to sustain its operation for >15h.

Sensorless Illumination Control of a Networked LED-Lighting System Using Feedforward Neural Network

In order to resolve the problem of energy hunger nowadays, saving lighting energy in buildings contributes an important part. In this paper, a sensorless illumination control scheme for smart networked LED lighting has been investigated. The scheme is based on a feedforward neural network to model all the nonlinear and linear relationships inside the lighting system as the controlled plant. Because the scheme does not rely on lighting simulation software, it is flexible to be implemented on microcontrollers. The scheme, moreover, can provide not only high accuracy in modeling but also global optimum in energy saving. Without using light sensors in its control loop, the approach can save significant cost and provide ease of installation as well. In addition, it also has the strength of fast response owing to feedforward control based on neural networks. The experimental results show that the approach can easily attain more than 95% modeling accuracy and also improve more than 28% energy saving with its optimal nonlinear multiple-input multiple-output control.

Fast-Transient Integrated Digital DC-DC Converter With Predictive and Feedforward Control

This paper introduces a dual-mode digital dc-dc converter combining the predictive and feedforward control with the conventional PID controller to achieve fast transient response and low overshoot. An additional predictive or jerk component is added to the conventional PID controller to speed up the transient response and reduce the settling time by approximately 50%. This predictive term is based on the second derivative of the error signal and introduces zero to the loop response which leads to increased bandwidth and improved phase margin. In addition, a feedforward control is also employed to further improve the transient by evaluating the change in the inductor current during the on and off time of the power transistors. Theoretical analysis and simulations were carried out to analyze the proposed design and algorithm. The proposed design is verified on silicon with a prototype of a digital dc-dc converter fabricated in CMOS 0.18 μ m process. The digital dc-dc converter achieved a settling time of 4 μs and an overshoot of 15 mV for a step-load transient of 450 mA, which are improved significantly as compared to the prior arts.

Energy consumption analysis of ZigBee-based energy harvesting wireless sensor networks

Energy consumption is one of the most important practical properties for deploying an energy harvesting wireless sensor network. This paper presents the energy consumption analysis of ZigBee-based energy harvesting wireless sensor networks to understand the energy requirements to the energy harvesting technologies from a practical aspect. Three kinds of energy consumption are analyzed to investigate the main factors influencing the design of energy harvesting technology: the energy consumption of end devices and of routers in power up mode, the energy consumption of data transmission with/without application acknowledgement requirements, and the energy consumption of routers supporting different node density. Based on the measurements, the key metrics influencing the lifetime of wireless sensor networks are summarized and an energy consumption model is proposed to evaluate the lifetime of a sensor node. Moreover, the potential energy-saving solutions are discussed for ZigBee-based energy harvesting wireless sensor networks.

Energy-aware wireless sensor network with ambient intelligence for smart LED lighting system control

In a typically commercial or residential building, the indoor lighting system can be considered as one of the largest single consuming units amongst many electrical loads. Saving energy in such a lighting system is utmost important whilst satisfying lighting users preference as well. This paper presents a controllable LED lighting system embedded with ambient intelligence gathered from a distributed smart wireless sensor network (WSN) to optimize and control the lighting system to be more efficient and user-oriented. In this proposed WSN-based control system, there is an inherent problem with the operational lifetime of its wireless sensor nodes limited by the finite energy capacity of their onboard batteries. To overcome this WSNs problem, an energy-aware communication protocol is introduced to reduce the power consumption of the WSN in order to prolong its operating time. The proposed smart WSN-based LED lighting system has been test-bedded and retrofitted into an existing workplace to conserve up to 10 % of waste light energy without compromising the workplace lighting condition. As for the operational lifetime of the proposed energy-aware WSN-based control system, the experimental results show that the wireless sensor nodes are able to operate for a longer period of time, from 87 days to 102 days, with about 20 % increase in their operational lifetimes.

A high frequency, high efficiency GaN HFET based inductive power transfer system

This paper aims to develop an Inductive Power Transfer (IPT) system targeting at Electric Vehicles (EV) and Hybrid Electric Vehicles (HEV). IPT systems provide significant benefits over conventional plug-in chargers. However, in order for IPT to be adopted for EV charging, efficiency is a key Figure of Merit (FOM) which needs to be achieved. This paper develops an inverter using Gallium Nitride (GaN) power transistors which have the benefit of low on-resistance and gate charge to reduce the switching and conduction loss. A design methodology for optimising the switching performance of the power transistor is developed in order to minimise switching loss while keeping overshoot under control. An efficiency centric control method is proposed to improve the efficiency of the system, while ensuring sufficient power transfer. The evaluation results show that a GaN based system is capable of outperforming a SiC based system. At a gap of 150mm, the system obtains above 90% efficiency at 1.3 kW. The system efficiency peaks at 95% at 100 kHz operation and 92% at 250 kHz operation at a distance of 80mm for 2kW output power.

Renewable energy integration into smart grids: Problems and solutions — Singapore experience

Singapore being a city state with 712.4km2, 5.183million people, S

Wearable Energy Harvesting System for Powering Wireless Devices

59,813 Gross Domestic Product (GDP) per capital, population density of 7,126 per square kilometer [1] and limited natural resources, identified her GHG emission as carbon dioxide (CO2) mainly from combustion of fossil fuels and natural gas to generate energy meeting development and human needs. By 2006 highly efficient combined cycle technology was deployed to generate 78% primary energy by burning natural gas [2]. One of Singapore key strategies to further mitigate GHG emissions is to increase the energy efficiency of various sectors and/or introducing renewable energy sources. Singapore Industry, Buildings and Households sector consumes 54%, 16% and 9%, respectively, of generated secondary energy [2]. Three areas of pilot test beddings are presented to enhance Singapore energy efficiency. First, Energy Market Authority (EMA) launched a pilot project Intelligent Energy System (IES) in 2010 to test and evaluate new applications and technologies around a smart grid, thereby enhancing Singapores power system efficiency and resilience, reducing wastage, saving peak loads and deferring capital investments to meet consumer demand in the future. Second, EMA will show case in 2012 how clean and renewable energy can be deployed at the system level in an environmentally, socially and economically sustainable manner for an off-grid community at Pulau Ubin, an island located at North East of Singapore. Third, Agency for Science, Technology and Research (A*STAR), and four industry partners developed a test facility Experimental Power Grid Centre (EPGC) [4], housing a 1MW power grid at Jurong Island in South West Singapore.

Illumination control of LED systems based on neural network model and energy optimization algorithm

As the world trends towards ageing population UN (2011), there is an increasing demand and interest in using technology to increase the quality of life for elderly people. An expanding area of interest is heading towards the health care applications like wearable biometric monitoring sensors. These monitoring nodes, typically powered by batteries, have various functions like sensing & monitoring bodily functions, after which the data is wirelessly transmitted to a remote data terminal Harry et al. (2009), Philippe et al. (2009). However such applications mentioned are not new, where earlier literatures envisioned of a not too distant future where e-textiles, electronics woven together with fabrics, are omni-present Marculescu et al. (2003). With improving technology in miniaturization and wireless communication, clothing containing sensors for sensing andmonitoring bodily physiological functionsWixted et al. (2007) is becoming more common and widespread. Such devices should be unobtrusive wearable, flexible, lightweight and ideally self-sufficient.