A large class of signals encountered in communications, biomedical engineering, renewable energy and power systems are conveniently processed in the complex domain C, where traditional adaptive signal processing in C is regarded as a straightforward extension of the corresponding algorithms in the real domain R. However, recent advances in widely linear modelling and augmented complex statistics show the suboptimality of such an assumption. In this work, based on the widely linear model, a class of linear and nonlinear adaptive filtering algorithms have been derived to process the generality of complex-valued signals (both second order circular and noncircular) in both noise-free and noisy environments, and their usefulness in real-world applications is demonstrated through case studies. The focus of this thesis is on the use of augmented second order statistics and widely linear modelling. The so called Augmented Complex Least Mean Square (ACLMS) algorithm has already been extended from the standard CLMS algorithm to perform optimum mean square error(MSE) type of adaptive estimation for the generality of complex-valued signals and has been shown to outperform the CLMS algorithm, however, a theoretical understanding of its performance is still missing. To this end, this thesis first addresses this issue in terms of both convergence analysis and steady state analysis. Next, based on the generalised framework introduced by the derivation of the ACLMS algorithm, a class of widely linear adaptive algorithms have been introduced; these include the Regularised Normalised ACLMS (RNACLMS) algorithm, the Augmented Affine Projection algorithm (AAPA) for linear Finite Impulse Response (FIR) adaptive filters, and also in the context of reservoir computing, for the recently introduced random state space based Echo State Networks (ESNs). Furthermore, the widely linear model has been introduced in the context of distributed networks, where the individual adaptive filters share information with their neighbours to achieve a cooperative estimation. The enhanced performances of the widely linear algorithms are illustrated in renewable energy and power system applications, in particular, for the prediction of wind profiles and frequency estimation of unbalanced three-phase power systems.

Accurate thermal analysis of axial flux permanent magnet (AFPM) machines is crucial in predicting maximum power output, and a number of heat transfer paths exist making it difficult to undertake a general analysis. Stator convective heat transfer is one of the most important and least investigated heat transfer mechanisms and therefore is the focus of the present work. Experimental measurements were undertaken using a thin-film electrical heating method based on a printed circuit board heater array, providing radially resolved steady state heat transfer data from an experimental rotor-stator system designed as a geometric mockup of a through-flow ventilated AFPM machine. Using a flat rotor, local Nusselt numbers Nu(r) = hR/k were measured across 0.6<r/R< 1, as a function of non-dimensional gap ratio 0.0106 < G < 0.0467 and rotational Reynolds number 3.7e4 < Re [Theta]1e6 where G = g/R and Re [Theta] = [omega]R2/[Nu]. Averaged results Nu were correlated with a power law and it was found that Nu [is approximately equal to] ARe0.7 [Theta] in the fully turbulent regime (Re [Theta] > 3e5), with A being a function of G. In the laminar regime, stator Nu was found to be similar to that of the free rotor. Transition at the stator occurred at Re [Theta] = 3e5 for all G and is particularly marked at G < 0.02. Increased Nusselt numbers at the periphery were always observed because of the ingress of ambient air along the stator due to the rotor pumping effect. A slotted rotor was also tested, and was found to improve stator heat transfer compared with a flat rotor. The measurements were compared with computational fluid dynamics simulations. These were found to give a conservative estimate of heat transfer, with inaccuracies near the edge (r/R > 0.85) and in the transitional flow regime. Predicted stator heat transfer was found to be relatively insensitive to the choice of turbulence model and the two-equation SST model was used for most of the simulations.

Empirical Mode Decomposition (EMD) is a data-driven method for the decomposition and time-frequency analysis of real world nonstationary signals. Its main advantages over other time-frequency methods are its locality, data-driven nature, multiresolution-based decomposition, higher time-frequency resolution and its ability to capture oscillation of any type (nonharmonic signals). These properties have made EMD a viable tool for real world nonstationary data analysis. Recent advances in sensor and data acquisition technologies have brought to light new classes of signals containing typically several data channels. Currently, such signals are almost invariably processed channel-wise, which is suboptimal. It is, therefore, imperative to design multivariate extensions of the existing nonlinear and nonstationary analysis algorithms as they are expected to give more insight into the dynamics and the interdependence between multiple channels of such signals. To this end, this thesis presents multivariate extensions of the empirical mode de- composition algorithm and illustrates their advantages with regards to multivariate non- stationary data analysis. Some important properties of such extensions are also explored, including their ability to exhibit wavelet-like dyadic filter bank structures for white Gaussian noise (WGN), and their capacity to align similar oscillatory modes from multiple data channels. Owing to the generality of the proposed methods, an improved multi- variate EMD-based algorithm is introduced which solves some inherent problems in the original EMD algorithm. Finally, to demonstrate the potential of the proposed methods, simulations on the fusion of multiple real world signals (wind, images and inertial body motion data) support the analysis.

By 2020, the majority of EU and US consumers will have smart meters installed. This thesis considers the exploitation of the potential to analyse smart metering data to help gain insight into the medium voltage electrical power systems' operating conditions, the low voltage electrical' power systems' topology information and consumers' power consumption habits. Distribution system state estimation can estimate a medium voltage system's electrical quantities. Its inputs involve transformers' loads that can be given by the aggregation of smart metering data. However, such loads have errors due to the lack of synchronisation among smart meters, the power loss in low voltage systems and the delay of meter data collection, so the subsequent impact of these three issues on the performance of the distribution system state estimation in MV systems are analysed, in order to determine whether they are barriers to gaining insight into these systems' operating conditions. The results show that: the first issue does not show a clear affect; the impact of the second issue is obvious; and the third issue significantly degrades the state estimation performance. To increase the insight into a low voltage system's topology, a phase identification method is designed and a topology identification technique is studied. The benefit of topology identification for state estimation is further assessed - limited benefit is shown, especially when the smart metering data used has low accuracy. The utility companies can provide specialised services to guide their electricity consumers to correctly control their own power demand, so power systems' energy efficiency may be increased. This requires the knowledge about consumers' power consumption habits. Therefore, in this work such knowledge is discovered from consumers' smart metering, socio-demographic and other data by three rule induction techniques. It is shown how, for example, those consumers with high potential for peak demand shifting can be targeted.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.