Practical assessment of hardware limitations on power aware wireless sensor networks - An anti-windup approach

This work considers the effect of hardware constraints that typically arise in practical power-aware wireless sensor network systems. A rigorous methodology is presented that quantifies the effect of output power limit and quantization constraints on bit error rate performance. The approach uses a novel, intuitively appealing means of addressing the output power constraint, wherein the attendant saturation block is mapped from the output of the plant to its input and compensation is then achieved using a robust anti-windup scheme. A priori levels of system performance are attained using a quantitative feedback theory approach on the initial, linear stage of the design paradigm. This hybrid design is assessed experimentally using a fully compliant 802.15.4 testbed where mobility is introduced through the use of autonomous robots. A benchmark comparison between the new approach and a number of existing strategies is also presented.

Resonant power conversion topologies for inductive charging of electrical vehicle batteries

This thesis is concerned with inductive charging of electric vehicle batteries. Rectified power form the 50/60 Hz utility feeds a dc-ac converter which delivers high-frequency ac power to the electric vehicle inductive coupling inlet. The inlet configuration has been defined by the Society of Automotive Engineers in Recommended Practice J-1773. This thesis studies converter topologies related to the series resonant converter. When coupled to the vehicle inlet, the frequency-controlled series-resonant converter results in a capacitively-filtered series-parallel LCLC (SP-LCLC) resonant converter topology with zero voltage switching and many other desirable features. A novel time-domain transformation analysis, termed Modal Analysis, is developed, using a state variable transformation, to analyze and characterize this multi-resonant fourth-orderconverter. Next, Fundamental Mode Approximation (FMA) Analysis, based on a voltage-source model of the load, and its novel extension, Rectifier-Compensated FMA (RCFMA) Analysis, are developed and applied to the SP-LCLC converter. The RCFMA Analysis is a simpler and more intuitive analysis than the Modal Analysis, and provides a relatively accurate closed-form solution for the converter behavior. Phase control of the SP-LCLC converter is investigated as a control option. FMA and RCFMA Analyses are used for detailed characterization. The analyses identify areas of operation, which are also validated experimentally, where it is advantageous to phase control the converter. A novel hybrid control scheme is proposed which integrates frequency and phase control and achieves reduced operating frequency range and improved partial-load efficiency. The phase-controlled SP-LCLC converter can also be configured with a parallel load and is an excellent option for the application. The resulting topology implements soft-switching over the entire load range and has high full-load and partial-load efficiencies. RCFMA Analysis is used to analyze and characterize the new converter topology, and good correlation is shown with experimental results. Finally, a novel single-stage power-factor-corrected ac-dc converter is introduced, which uses the current-source characteristic of the SP-LCLC topology to provide power factor correction over a wide output power range from zero to full load. This converter exhibits all the advantageous characteristics of its dc-dc counterpart, with a reduced parts count and cost. Simulation and experimental results verify the operation of the new converter.

High-current inductors for high-power automotive DC-DC converters

This thesis is focused on the investigation of magnetic materials for high-power dcdc converters in hybrid and fuel cell vehicles and the development of an optimized high-power inductor for a multi-phase converter. The thesis introduces the power system architectures for hybrid and fuel cell vehicles. The requirements for power electronic converters are established and the dc-dc converter topologies of interest are introduced. A compact and efficient inductor is critical to reduce the overall cost, weight and volume of the dc-dc converter and optimize vehicle driving range and traction power. Firstly, materials suitable for a gapped CC-core inductor are analyzed and investigated. A novel inductor-design algorithm is developed and automated in order to compare and contrast the various magnetic materials over a range of frequencies and ripple ratios. The algorithm is developed for foil-wound inductors with gapped CC-cores in the low (10 kHz) to medium (30 kHz) frequency range and investigates the materials in a natural-convection-cooled environment. The practical effects of frequency, ripple, air-gap fringing, and thermal configuration are investigated next for the iron-based amorphous metal and 6.5 % silicon steel materials. A 2.5 kW converter is built to verify the optimum material selection and thermal configuration over the frequency range and ripple ratios of interest. Inductor size can increase in both of these laminated materials due to increased airgap fringing losses. Distributing the airgap is demonstrated to reduce the inductor losses and size but has practical limitations for iron-based amorphous metal cores. The effects of the manufacturing process are shown to degrade the iron-based amorphous metal multi-cut core loss. The experimental results also suggest that gap loss is not a significant consideration in these experiments. The predicted losses by the equation developed by Reuben Lee and cited by Colonel McLyman are significantly higher than the experimental results suggest. Iron-based amorphous metal has better preformance than 6.5 % silicon steel when a single cut core and natural-convection-cooling are used. Conduction cooling, rather than natural convection, can result in the highest power density inductor. The cooling for these laminated materials is very dependent on the direction of the lamination and the component mounting. Experimental results are produced showing the effects of lamination direction on the cooling path. A significant temperature reduction is demonstrated for conduction cooling versus natural-convection cooling. Iron-based amorphous metal and 6.5% silicon steel are competitive materials when conduction cooled. A novel inductor design algorithm is developed for foil-wound inductors with gapped CC-cores for conduction cooling of core and copper. Again, conduction cooling, rather than natural convection, is shown to reduce the size and weight of the inductor. The weight of the 6.5 % silicon steel inductor is reduced by around a factor of ten compared to natural-convection cooling due to the high thermal conductivity of the material. The conduction cooling algorithm is used to develop high-power custom inductors for use in a high power multi-phase boost converter. Finally, a high power digitally-controlled multi-phase boost converter system is designed and constructed to test the high-power inductors. The performance of the inductors is compared to the predictions used in the design process and very good correlation is achieved. The thesis results have been documented at IEEE APEC, PESC and IAS conferences in 2007 and at the IEEE EPE conference in 2008.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Algorithms for indoor localization based on IEEE 802.15.4-2011 UWB and inertial sensors

In this thesis, extensive experiments are firstly conducted to characterize the performance of using the emerging IEEE 802.15.4-2011 ultra wideband (UWB) for indoor localization, and the results demonstrate the accuracy and precision of using time of arrival measurements for ranging applications. A multipath propagation controlling technique is synthesized which considers the relationship between transmit power, transmission range and signal-to-noise ratio. The methodology includes a novel bilateral transmitter output power control algorithm which is demonstrated to be able to stabilize the multipath channel, and enable sub 5cm instant ranging accuracy in line of sight conditions. A fully-coupled architecture is proposed for the localization system using a combination of IEEE 802.15.4-2011 UWB and inertial sensors. This architecture not only implements the position estimation of the object by fusing the UWB and inertial measurements, but enables the nodes in the localization network to mutually share positional and other useful information via the UWB channel. The hybrid system has been demonstrated to be capable of simultaneous local-positioning and remote-tracking of the mobile object. Three fusion algorithms for relative position estimation are proposed, including internal navigation system (INS), INS with UWB ranging correction, and orientation plus ranging. Experimental results show that the INS with UWB correction algorithm achieves an average position accuracy of 0.1883m, and gets 83% and 62% improvements on the accuracy of the INS (1.0994m) and the existing extended Kalman filter tracking algorithm (0.5m), respectively.

Hybrid photonic crystal lasers

Energy efficient Wavelength Division Multiplexing (WDM) is the key to satisfying the future bandwidth requirements of datacentres. As the silicon photonics platform is regarded the only technology able to meet the required power and cost efficiency levels, the development of silicon photonics compatible narrow linewidth lasers is now crucial. We discuss the requirements for such laser systems and report the experimental demonstration of a compact uncooled external-cavity mW-class laser architecture with a tunable Si Photonic Crystal resonant reflector, suitable for direct Frequency Modulation.