Detection of Power Disturbances for Power Quality Monitoring Using Mathematical Morphology

In power quality monitoring, determining the type of power quality disturbance occurring in the power system is important. Some disturbances such as, voltage dip, momentary interruption, voltage swell, or oscillatory transients in power systems may result internal-function or failure in the operation of some devices. Knowing the location where the disturbances occur in the system can yield an effective and efficient result when an appropriate method is applied in the attempt to solve the power quality issues. Some traditional strategies such as, wavelet or Fast Fourier transforms have been applied to detect and locate power quality disturbances, suffer from the complexity of the algorithm and the calculation load. In this thesis, mathematical morphology has been investigated for this purpose due to the merits of robustness and the simple calculations needed. In this thesis, some novel strategies using mathematical morphology are presented to find the time location of the disturbances, that is defined as the start and end points when the disturbance occur in the time domain. The first method was using morphology gradient, top-hat transform, and Skeletonization to identify the time location of the disturbances and noise in the system, and then plotting the results in 3D for pattern recognition. This Skeletonization is also combined with Morphology Edge Detection to find the accurate time location of disturbances in the system for both noise free signals and signals with noise. The overall result shows the reduction of the error was significant compared to the result of morphology edge detection strategy. Another novel strategy is presented by converting a signal to an image then applying image processing techniques, which are then evaluated using a control chart to find the time location of any disturbances. This conversion strategy is also applied for detecting the times of power quality disturbances uses short data samples of the signal (4 samples), so that it can be implemented as a real time detection strategy. The results show an accurate strategy in detecting disturbances. Half Multi-resolution Morphology Gradients (HMMG) based on multi-resolution morphology gradients (MMG) is also presented as a novel strategy and it operates in level 1 only, reducing the processing and increase the speed of detection of disturbance. The results show accurate detection when disturbances occur in the system. Other applications of MM are also presented such as a new alternative method in estimating the frequency in a signal based on top-hat and bottom-hat transforms with the results showing the ability of this method to handle low frequencies when the signal is a noise free signal. Neural networks are also implemented with MM for the identification and classification of disturbances. All the novel strategies using Skeletonization, signal/image conversion and HMMG for disturbances detection were then evaluated using a real dataset and an experimental dataset. Overall results show that this three methods can detect disturbances accurately

Optical and radio survey of Southern Compact Groups of galaxies. I. Pilot study of six groups

Multi-wavelength observations of Hickson's Compact Groups (HCGs) have shown that many of these groups are physical bound structures and are in different stage of evolution, from spiral-dominated systems to almost merged objects. Very few studies have analysed the Southern Compact Groups (SCGs) sample, which is though to be younger that HCGs, due to an on average higher number of spiral galaxies. We present here the first results from optical and radio observations on a pilot sample of SCGs.Comment: accepted on A&A on July 19, 2007. Figures 1 and 3-12 will be available only in electronic for

Power Quality Management and Classification for Smart Grid Application using Machine Learning

The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development

Starbursts versus Truncated Star Formation in Nearby Clusters of Galaxies

We present long-slit spectroscopy, B and R bandpass imaging, and 21 cm observations of a sample of early-type galaxies in nearby clusters which are known to be either in a star-forming phase or to have had star formation which recently terminated. From the long-slit spectra, obtained with the Blanco 4-m telescope, we find that emission lines in the star-forming cluster galaxies are significantly more centrally concentrated than in a sample of field galaxies. The broadband imaging reveals that two currently star-forming early-type galaxies in the Pegasus I cluster have blue nuclei, again indicating that recent star formation has been concentrated. In contrast, the two galaxies for which star formation has already ended show no central color gradient. The Pegasus I galaxy with the most evident signs of ongoing star formation (NGC7648), exhibits signatures of a tidal encounter. Neutral hydrogen observations of that galaxy with the Arecibo radiotelescope reveal the presence of ~4 x 10^8 solar masses of HI. Arecibo observations of other current or recent star-forming early-type galaxies in Pegasus I indicate smaller amounts of gas in one of them, and only upper limits in others.Comment: to be published in Astronomical Journa

Development of Advanced Mathematical Morphology Algorithms and their Application to the Detection of Disturbances in Power Systems

This thesis is concerned with the development of Mathematical morphology (MM)-based algorithms and their applications to signal processing in power systems, including typical power quality disturbances such as low frequency oscillations (LFO) and harmonics. Traditional morphological operators are extended to advanced ones in the thesis, including multi-resolution morphological gradient (MMG) algorithms, envelope extraction morphological filters (MF), LFO extraction MF and convolved morphological filters (CMF). These advanced morphological operators are applied to the detection and classification of power disturbances, detection of continuous and damped LFO, and the detection and removal of harmonics in power systems

Solar Magnetic Feature Detection and Tracking for Space Weather Monitoring

We present an automated system for detecting, tracking, and cataloging emerging active regions throughout their evolution and decay using SOHO Michelson Doppler Interferometer (MDI) magnetograms. The SolarMonitor Active Region Tracking (SMART) algorithm relies on consecutive image differencing to remove both quiet-Sun and transient magnetic features, and region-growing techniques to group flux concentrations into classifiable features. We determine magnetic properties such as region size, total flux, flux imbalance, flux emergence rate, Schrijver's R-value, R* (a modified version of R), and Falconer's measurement of non-potentiality. A persistence algorithm is used to associate developed active regions with emerging flux regions in previous measurements, and to track regions beyond the limb through multiple solar rotations. We find that the total number and area of magnetic regions on disk vary with the sunspot cycle. While sunspot numbers are a proxy to the solar magnetic field, SMART offers a direct diagnostic of the surface magnetic field and its variation over timescale of hours to years. SMART will form the basis of the active region extraction and tracking algorithm for the Heliophysics Integrated Observatory (HELIO)