A dual-band antenna enabling improved quality of service in multi-radio wireless sensor applications in indoor environments

Science Foundation Ireland (CSET - Centre for Science, Engineering and Technology, Grant No. 07/CE/11147)

Empirical analysis and improved modelling of natural gas demand in Ireland

Countries across the world are being challenged to decarbonise their energy systems in response to diminishing fossil fuel reserves, rising GHG emissions and the dangerous threat of climate change. There has been a renewed interest in energy efficiency, renewable energy and low carbon energy as policy‐makers seek to identify and put in place the most robust sustainable energy system that can address this challenge. This thesis seeks to improve the evidence base underpinning energy policy decisions in Ireland with a particular focus on natural gas, which in 2011 grew to have a 30% share of Ireland’s TPER. Natural gas is used in all sectors of the Irish economy and is seen by many as a transition fuel to a low-carbon energy system; it is also a uniquely excellent source of data for many aspects of energy consumption. A detailed decomposition analysis of natural gas consumption in the residential sector quantifies many of the structural drives of change, with activity (R2 = 0.97) and intensity (R2 = 0.69) being the best explainers of changing gas demand. The 2002 residential building regulations are subject to an ex-post evaluation, which using empirical data finds a 44 ±9.5% shortfall in expected energy savings as well as a 13±1.6% level of non-compliance. A detailed energy demand model of the entire Irish energy system is presented together with scenario analysis of a large number of energy efficiency policies, which show an aggregate reduction in TFC of 8.9% compared to a reference scenario. The role for natural gas as a transition fuel over a long time horizon (2005-2050) is analysed using an energy systems model and a decomposition analysis, which shows the contribution of fuel switching to natural gas to be worth 12 percentage points of an overall 80% reduction in CO2 emissions. Finally, an analysis of the potential for CCS in Ireland finds gas CCS to be more robust than coal CCS for changes in fuel prices, capital costs and emissions reduction and the cost optimal location for a gas CCS plant in Ireland is found to be in Cork with sequestration in the depleted gas field of Kinsale.

Quantifying the value of improved wind energy forecasts in a pool-based electricity market

This work illustrates the influence of wind forecast errors on system costs, wind curtailment and generator dispatch in a system with high wind penetration. Realistic wind forecasts of different specified accuracy levels are created using an auto-regressive moving average model and these are then used in the creation of day-ahead unit commitment schedules. The schedules are generated for a model of the 2020 Irish electricity system with 33% wind penetration using both stochastic and deterministic approaches. Improvements in wind forecast accuracy are demonstrated to deliver: (i) clear savings in total system costs for deterministic and, to a lesser extent, stochastic scheduling; (ii) a decrease in the level of wind curtailment, with close agreement between stochastic and deterministic scheduling; and (iii) a decrease in the dispatch of open cycle gas turbine generation, evident with deterministic, and to a lesser extent, with stochastic scheduling.

Improved kinematic sensing for motion control applications

New compensation methods are presented that can greatly reduce the slit errors (i.e. transition location errors) and interval errors induced due to non-idealities in optical incremental encoders (square-wave). An M/T-type, constant sample-time digital tachometer (CSDT) is selected for measuring the velocity of the sensor drives. Using this data, three encoder compensation techniques (two pseudoinverse based methods and an iterative method) are presented that improve velocity measurement accuracy. The methods do not require precise knowledge of shaft velocity. During the initial learning stage of the compensation algorithm (possibly performed in-situ), slit errors/interval errors are calculated through pseudoinversebased solutions of simple approximate linear equations, which can provide fast solutions, or an iterative method that requires very little memory storage. Subsequent operation of the motion system utilizes adjusted slit positions for more accurate velocity calculation. In the theoretical analysis of the compensation of encoder errors, encoder error sources such as random electrical noise and error in estimated reference velocity are considered. Initially, the proposed learning compensation techniques are validated by implementing the algorithms in MATLAB software, showing a 95% to 99% improvement in velocity measurement. However, it is also observed that the efficiency of the algorithm decreases with the higher presence of non-repetitive random noise and/or with the errors in reference velocity calculations. The performance improvement in velocity measurement is also demonstrated experimentally using motor-drive systems, each of which includes a field-programmable gate array (FPGA) for CSDT counting/timing purposes, and a digital-signal-processor (DSP). Results from open-loop velocity measurement and closed-loop servocontrol applications, on three optical incremental square-wave encoders and two motor drives, are compiled. While implementing these algorithms experimentally on different drives (with and without a flywheel) and on encoders of different resolutions, slit error reductions of 60% to 86% are obtained (typically approximately 80%).