Modelling and control of a floating oscillating water column

A novel numerical model of a Bent Backwards Duct Buoy (BBDB) Oscillating Water Column (OWC) Wave Energy Converter was created based on existing isolated numerical models of the different energy conversion systems utilised by an OWC. The novel aspect of this numerical model is that it incorporates the interdependencies of the different power conversion systems rather than modelling each system individually. This was achieved by accounting for the dynamic aerodynamic damping caused by the changing turbine rotational velocity by recalculating the turbine damping for each simulation sample and applying it via a feedback loop. The accuracy of the model was validated using experimental data collected during the Components for Ocean Renewable Energy Systems (CORES) EU FP-7 project that was tested in Galway Bay, Ireland. During the verification process, it was discovered that the model could also be applied as a valuable tool when troubleshooting device performance. A new turbine was developed and added to a full scale model after being investigated using Computational Fluid Dynamics. The energy storage capacity of the impulse turbine was investigated by modelling the turbine with both high and low inertia and applying three turbine control theories to the turbine using the full scale model. A single Maximum Power Point Tracking algorithm was applied to the low-inertia turbine, while both a fixed and dynamic control algorithm was applied to the high-inertia turbine. These results suggest that the highinertia turbine could be used as a flywheel energy storage device that could help minimize output power variation despite the low operating speed of the impulse turbine. This research identified the importance of applying dynamic turbine damping to a BBDB OWC numerical model, revealed additional value of the model as a device troubleshooting tool, and found that an impulse turbine could be applied as an energy storage system.

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.

Optimisation of Smart Grid performance using centralised and distributed control techniques

A massive change is currently taking place in the manner in which power networks are operated. Traditionally, power networks consisted of large power stations which were controlled from centralised locations. The trend in modern power networks is for generated power to be produced by a diverse array of energy sources which are spread over a large geographical area. As a result, controlling these systems from a centralised controller is impractical. Thus, future power networks will be controlled by a large number of intelligent distributed controllers which must work together to coordinate their actions. The term Smart Grid is the umbrella term used to denote this combination of power systems, artificial intelligence, and communications engineering. This thesis focuses on the application of optimal control techniques to Smart Grids with a focus in particular on iterative distributed MPC. A novel convergence and stability proof for iterative distributed MPC based on the Alternating Direction Method of Multipliers is derived. Distributed and centralised MPC, and an optimised PID controllers' performance are then compared when applied to a highly interconnected, nonlinear, MIMO testbed based on a part of the Nordic power grid. Finally, a novel tuning algorithm is proposed for iterative distributed MPC which simultaneously optimises both the closed loop performance and the communication overhead associated with the desired control.

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%).