Surya Santoso

In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. The approach is based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. The key idea underlying the approach is to decompose a given disturbance signal into other signals which represent a smoothed version and a detailed version of the original signal. The decomposition is performed using multiresolution signal decomposition techniques. We demonstrate and test our proposed technique to detect and localize disturbances with actual power line disturbances. In order to enhance the detection outcomes, we utilize the squared wavelet transform coefficients of the analyzed power line signal. Based on the results of the detection and localization, we carry out an initial investigation of the ability to uniquely characterize various types of power quality disturbances. This investigation is based on characterizing the uniqueness of the squared wavelet transform coefficients for each power quality disturbance.

This paper aims to understand, identify, and mitigate the impacts of residential electric vehicle (EV) charging on distribution system voltages. A thorough literature review on the impacts of residential EV charging is presented, followed by a proposed method for evaluating the impacts of EV loads on the distribution system voltage quality. Practical solutions to mitigate EV load impacts are discussed as well, including infrastructural changes and indirect controlled charging with time-of-use (TOU) pricing. An optimal TOU schedule is also presented, with the aim of maximizing both customer and utility benefits. This paper also presents a discussion on implementing smart charging algorithms to directly control EV charging rates and EV charging starting times. Finally, a controlled charging algorithm is proposed to improve the voltage quality at the EV load locations while avoiding customer inconvenience. The proposed method significantly decreases the impacts of EV load charging on system peak load demand and feeder voltages.

It is the objective of this paper to present unique features that characterize power quality events and methodologies to extract them from recorded voltage and/or current waveforms using Fourier and wavelet transforms. Examples of unique features include peak amplitudes, RMS, frequency, and statistics of wavelet transform coefficients. These features are derived from well documented theories, power engineers' heuristics gained through long years of experience, and power quality data collected in recent years. Converter operation, transformer energization, and capacitor energization (which includes normal, back-to-back, and re-strike on opening energization), representing three common power quality events at the distribution level, are presented. These examples provide the basis for further characterization of other power quality events.

In this paper, the authors present a wavelet compression technique for power quality disturbance data. The compression technique is performed through signal decomposition, thresholding of wavelet transform coefficients and signal reconstruction. Threshold values are determined by weighting the absolute maximum value at each scale. Wavelet transform coefficients whose values are below the threshold are discarded, while those that are above the threshold are kept along with their temporal locations. The authors show the efficacy of the technique by compressing actual disturbance data. The file size of the compressed data is only one-sixth to one-third that of the original data. Therefore, the cost related to storing and transmitting the data is significantly reduced.

Existing techniques for recognizing and identifying power quality disturbance waveforms are primarily based on visual inspection of the waveform. It is the purpose of this paper to bring to bear advances, especially in wavelet transforms, artificial neural networks, and the mathematical theory of evidence, to the problem of automatic power quality disturbance waveform recognition. Unlike past attempts to automatically identify disturbance waveforms where the identification is performed in the time domain using an individual artificial neural network, the proposed recognition scheme is carried out in the wavelet domain using a set of multiple neural networks. The outcomes of the networks are then integrated using decision making schemes such as a simple voting scheme or the Dempster-Shafer theory of evidence. With such a configuration, the classifier is capable of providing a degree of belief for the identified disturbance waveform.