A survey of techniques applied to non-stationary waveforms in electrical power systems

The well-known and ever-present time-varying and non-stationary nature of waveforms in power systems requires a comprehensive and precise analytical basis that needs to be incorporated in the system studies and analyses. This time-varying behavior is due to continuous changes in system configurations, linear load levels and operating modes of nonlinear load / equipment and thus present conceptual and practical challenges. The objective of this paper is to provide a comprehensive bibliographical survey of the proposed techniques to deal with time-varying and non-stationary waveforms in power systems

Widely Linear State Space Filtering of Improper Complex Signals

Complex signals are the backbone of many modern applications, such as power systems, communication systems, biomedical sciences and military technologies. However, standard complex valued signal processing approaches are suited to only a subset of complex signals known as proper, and are inadequate of the generality of complex signals, as they do not fully exploit the available information. This is mainly due to the inherent blindness of the algorithms to the complete second order statistics of the signals, or due to under-modelling of the underlying system. The aim of this thesis is to provide enhanced complex valued, state space based, signal processing solutions for the generality of complex signals and systems. This is achieved based on the recent advances in the so called augmented complex statistics and widely linear modelling, which have brought to light the limitations of conventional statistical complex signal processing approaches. Exploiting these developments, we propose a class of widely linear adaptive state space estimation techniques, which provide a unified framework and enhanced performance for the generality of complex signals, compared with conventional approaches. These include the linear and nonlinear Kalman and particle filters, whereby it is shown that catering for the complete second order information and system models leads to significant performance gains. The proposed techniques are also extended to the case of cooperative distributed estimation, where nodes in a network collaborate locally to estimate signals, under a framework that caters for general complex signals, as well as the cross-correlations between observation noises, unlike earlier solutions. The analysis of the algorithms are supported by numerous case studies, including frequency estimation in three phase power systems, DIFAR sonobuoy underwater target tracking, and real-world wind modeling and prediction.Open Acces

Multiresolution optimal interpolation and statistical analysis of topex/podeidon satellite altimetry

Includes bibliographical references (p. 18-20).Supported by the Office of Naval Research. N00014-91-J-1004 Supported by the Draper Laboratory. DL-H-467133 Supported by the Air Force Office of Scientific Research. F49620-92-J-0002 Supported by the Natural Sciences and Engineering Research Council of Canada. NSERC-67 Supported by NASA. NAGW-1048Paul W. Fieguth ... [et al.]