Event-related EEG/MEG synchronization and desynchronization: basic principles.

Renewable energy sources like wind energy are copiously available without any limitation. Wind turbines are used to tap the potential of wind energy, which is available in millions of MW. Reliability of wind turbine is critical to extract this maximum amount of energy from the wind. We reviewed different techniques, methodologies and algorithms developed to monitor the performance of wind turbine as well as for an early fault detection to keep away the wind turbines from catastrophic conditions due to sudden breakdowns. To keep the wind turbine in operation, implementation of condition monitoring system (CMS) and fault detection system (FDS) is paramount and for this purpose ample knowledge of these two types of systems is mandatory. So, an attempt has been made in this direction to review maximum approaches related to CMS and FDS in this piece of writing.

Condition monitoring and fault detection of wind turbines and related algorithms: A review

1. Introduction 2. Observation techniques 3. Description of ocean waves 4. Statistics 5. Linear wave theory (oceanic waters) 6. Waves in oceanic waters 7. Linear wave theory (coastal waters) 8. Waves in coastal waters 9. The SWAN wave model Appendices References Index.

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Waves in Oceanic and Coastal Waters

An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency bandspecific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments. q 1999 Elsevier Science Ireland Ltd. All rights reserved.

Event-related EEG/MEG synchronization and desynchronization: Basic principles

A method is presented for identification of linear, time-variant, nonminimum phase systems when only output data are available. The input sequence need not be independent, but it must be non-Gaussian, with some special properties described in the test. The authors model a finite-dimensional system as an ARMA (autoregressive moving-average) rational function of known orders, but the special cases of AR, MA, and all-pass models are also considered. To estimate the parameters of their model, the authors utilize both second- and higher-order statistics of the output, which may be contaminated by additive, zero-mean, Gaussian white noise of unknown variance. The parameter estimators obtained are proved, under mild conditions, to be consistent. Simulations verify the performance of the proposed method in the case of relatively low signal-to-noise ratios, and when there is a model-order mismatch. >

Identification of nonminimum phase systems using higher order statistics

The bispectrum, which is an ensemble average of a product of three spectral components, is shown to be a very useful diagnostic tool in experimental studies of nonlinear wave interactions in random media. In particular, it is shown that the bicoherence spectrum may be used to discriminate between nonlinearly coupled waves and spontaneously excited waves and to measure the fraction of wave power due to the quadratic wave coupling in a self-excited fluctuation spectrum. Practical aspects of digital bispectral analysis techniques, such as estimation and statistical variability of the estimator, are also discussed. Finally, applications of bispectral analysis techniques in the analysis and interpretation of plasma fluctuation data are described.

Young C. Kim

Papers

Digital Bispectral Analysis and Its Applications to Nonlinear Wave Interactions