Parametric Analysis and Bandwidth Optimisation of Hybrid Linear-exponential Tapered Slot Vivaldi Antennas

This work presents an analysis of the effects of a hybrid linear-exponential tapered slot on the key properties of both the antipodal and co-planar Vivaldi antennas at low frequencies using parametric analysis and Nonlinear Sequential Programming optimisation. It was observed that the hybrid tapered slot can extend the lower frequency limit of the antipodal Vivaldi antenna however with slight deterioration of the gain and E-plane radiation pattern. On the other hand, the optimisation of the hybrid and conventional tapered slot co-planar Vivaldi antennas converged to antennas with the same performance results

Forecasting hot water consumption in dwellings using artificial neural networks

The electricity grid is currently transforming and becoming more and more decentralised. Green energy generation has many incentives throughout the world thus small renewable generation units become popular. Intermittent generation units pose threat to system stability so new balancing techniques like Demand Side Management must be researched. Residential hot water heaters are perfect candidates to be used for shifting electricity consumption in time. This paper investigates the ability on Artificial Neural Networks to predict individual hot water heater energy demand profile. Data from about a hundred dwellings are analysed using autocorrelation technique. The most appropriate lags were chosen and different Neural Network model topologies were tested and compared. The results are positive and show that water heaters have could potentially shift electric energy

A rare-earth free SHEV powertrain and its control

A topology of a candidate rare-earth free Series Hybrid Electric Vehicle (SHEV) powertrain and the coordinated control of its components is presented in this paper. The powertrain is fed with a field controlled synchronous generator and a controlled battery bank and drives a 60 kW rare-earth free traction motor. Simulation results are presented for normal operating conditions and two faulted-mode operating scenarios where the power electronic converter in the system is faulted are investigated

Forecasting hot water consumption in residential houses

An increased number of intermittent renewables poses a threat to the system balance. As a result, new tools and concepts, like advanced demand-side management and smart grid technologies, are required for the demand to meet supply. There is a need for higher consumer awareness and automatic response to a shortage or surplus of electricity. The distributed water heater can be considered as one of the most energy-intensive devices, where its energy demand is shiftable in time without influencing the comfort level. Tailored hot water usage predictions and advanced control techniques could enable these devices to supply ancillary energy balancing services. The paper analyses a set of hot water consumption data from residential dwellings. This work is an important foundation for the development of a demand-side management strategy based on hot water consumption forecasting at the level of individual residential houses. Various forecasting models, such as exponential smoothing, seasonal autoregressive integrated moving average, seasonal decomposition and a combination of them, are fitted to test different prediction techniques. These models outperform the chosen benchmark models (mean, naive and seasonal naive) and show better performance measure values. The results suggest that seasonal decomposition of the time series plays the most significant part in the accuracy of forecasting

Hexagonal uniformly redundant arrays (HURAs) for scintillator based coded aperture neutron imaging

A series of Monte Carlo simulations have been conducted, making use of the EJ-426 neutron scintillator detector, to investigate the potential of using hexagonal uniformly redundant arrays (HURAs) for scintillator based coded aperture neutron imaging. This type of scintillator material has a low sensitivity to gamma rays, therefore, is of particular use in a system with a source that emits both neutrons and gamma rays. The simulations used an AmBe source, neutron images have been produced using different coded-aperture materials (boron-10, cadmium-113 and gadolinium-157) and location error has also been estimated. In each case the neutron image clearly shows the location of the source with a relatively small location error. Neutron images with high resolution can be easily used to identify and locate nuclear materials precisely in nuclear security and nuclear decommissioning applications

Detecting energy dependent neutron capture distributions in a liquid scintillator

A novel technique is being developed to estimate the effective dose of a neutron field based on the distribution of neutron captures in a scintillator. Using Monte Carlo techniques, a number of monoenergetic neutron source energies and locations were modelled and their neutron capture response was recorded. Using back propagation Artificial Neural Networks (ANN) the energy and incident direction of the neutron field was predicted from the distribution of neutron captures within a 6Li-loaded liquid scintillator. Using this proposed technique, the effective dose of 252Cf, 241AmBe and 241AmLi neutron fields was estimated to within 30% for four perpendicular angles in the horizontal plane. Initial theoretical investigations show that this technique holds some promise for real-time estimation of the effective dose of a neutron field

Managing renewable intermittency in smart grid:use of residential hot water heaters as a form of energy storage

This paper discusses a novel wind generation balancing technique to improve renewable energy integration to the system. Novel individual hot water heater controllers were modelled with the ability to forecast and look ahead the required energy, while responding to electricity grid imbalance. Artificial intelligence and machine learning techniques were used to learn and predict energy usage. In this research wind power data was used in most cases to represent the supply side, where focus was on the actual generation deviation from plan. It proved to be possible to balance the generation and increase system efficiency while maintaining user satisfaction. The methods developed in this research are not limited to wind power balancing and can also be used with any other type of renewable generation source

Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward

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