M.D. Cox

Bio: M.D. Cox is an academic researcher from Louisiana Tech University. The author has contributed to research in topics: Power factor & AC power. The author has an hindex of 11, co-authored 17 publications receiving 1149 citations. Previous affiliations of M.D. Cox include Louisiana State University.

Practical definitions for powers in systems with nonsinusoidal waveforms and unbalanced loads: a discussion

Abstract: Existing definitions for power terms in alternating current systems work well for single-phase and three-phase systems where both voltages and currents are sinusoidal with respect to time. This paper clarifies and proposes definitions for power terms that are practical and effective when voltage and/or currents are distorted and/or unbalanced. It also suggests definitions for measurable values that may be used to indicate the level of distortion and unbalance.

Discrete wavelet analysis of power system transients

Abstract: Wavelet analysis is a new method for studying power system transients. Through wavelet analysis, transients are decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave frequency band. This paper presents the basic ideas of discrete wavelet analysis. A variety of actual and simulated transient signals are then analyzed using the discrete wavelet transform that help demonstrate the power of wavelet analysis.

Frequency characteristics of wavelets

Abstract: Wavelets detect and locate time of disturbances successfully, but for measurement of power/energy they also have to estimate and classify them accurately. This paper investigates the factors on choice of a certain wavelet function and qualitatively shows how the number of coefficients of the wavelets is an important number that affects output decomposition and energy distribution leakage. Wavelets provide an output in terms of the time-frequency scale. The frequency bandwidth characteristics of these individual wavelet levels provide better understanding of the wavelets. The sampling frequency and the number of data points are important parameters and must be carefully selected to avoid the frequency of interest falling into the end regions.

Discussion of power definitions contained in the IEEE Dictionary

Abstract: This tutorial paper discusses the drawbacks of the definitions of various types of powers found in the IEEE Standard Dictionary of Electrical and Electronics Terms (IEEE Std. 100-88). With the exceptions of instantaneous power and active power, all remaining kinds of "powers" are nonphysical. The concept of power factor in polyphase circuits is ambiguous. Examples that illustrate the shortcomings of many power definitions are included. The impact of these definitions on current power/energy metering practices are discussed. It is recommended that some definitions be either changed or eliminated from the IEEE Dictionary. >

A New Static Var Compensator for an Arc Furnace

Abstract: This paper presents the design of an electronic compensator that can compensate the reactive current drawn by an arc furnace. Consisting of three independent single-phase pulse-width modulated (PWM) inverters, the proposed compensator responds quickly to any sudden load changes and compensates both the fundamental displacement current and the harmonic distortion current drawn by the load. Unlike conventional static var compensators, the electronic compensator can also supply the active current demanded by the load if an energy source independent of the utility is available. Simulations are conducted, including the modelling of the furnace current and voltage waveforms during the early stage of scrap melting, in order to determine the effectiveness of several suggested detection methods that can be used to separate the active and reactive components of the furnace current. Actual waveforms observed on a local furnace are included that corroborate the computer modelling. Experimental results indicate that the electronic compensator equipped with a suitable detection circuit can accurately compensate an arc furnace.

A review of active filters for power quality improvement

Abstract: Active filtering of electric power has now become a mature technology for harmonic and reactive power compensation in two-wire (single phase), three-wire (three phase without neutral), and four-wire (three phase with neutral) AC power networks with nonlinear loads. This paper presents a comprehensive review of active filter (AF) configurations, control strategies, selection of components, other related economic and technical considerations, and their selection for specific applications. It is aimed at providing a broad perspective on the status of AF technology to researchers and application engineers dealing with power quality issues. A list of more than 200 research publications on the subject is also appended for a quick reference.

Feature Extraction and Selection for Emotion Recognition from EEG

Abstract: Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.

Comparison of Control Strategies for Shunt Active Power Filters in Three-Phase Four-Wire Systems

Abstract: Strategies for extracting the three-phase reference currents for shunt active power filters are compared, evaluating their performance under different source and load conditions with the new IEEE Standard 1459 power definitions. The study was applied to a three-phase four-wire system in order to include imbalance. Under balanced and sinusoidal voltages, harmonic cancellation and reactive power compensation can be attained in all the methods. However, when the voltages are distorted and/or unbalanced, the compensation capabilities are not equivalent, with some strategies unable to yield an adequate solution when the mains voltages are not ideal. Simulation and experimental results are included

Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition

Abstract: Motor-current-signature analysis has been successfully used in induction machines for fault diagnosis. The method, however, does not always achieve good results when the speed or the load torque is not constant, because this causes variations on the motor-slip and fast Fourier transform problems appear due to a nonstationary signal. This paper proposes a new method for motor fault detection, which analyzes the spectrogram based on a short-time Fourier transform and a further combination of wavelet and power-spectral-density (PSD) techniques, which consume a smaller amount of processing power. The proposed algorithms have been applied to detect broken rotor bars as well as shorted turns. Besides, a merit factor based on PSD is introduced as a novel approach for condition monitoring, and a further implementation of the algorithm is proposed. Theoretical development and experimental results are provided to support the research.

Classification of human emotion from EEG using discrete wavelet transform

Abstract: In this paper, we summarize the human emotion recognition using different set of electroencephalogram (EEG) channels using discrete wavelet transform. An audio-visual induction based protocol has been designed with more dynamic emotional content for inducing discrete emotions (disgust, happy, surprise, fear and neutral). EEG signals are collected using 64 electrodes from 20 subjects and are placed over the entire scalp using International 10-10 system. The raw EEG signals are preprocessed using Surface Laplacian (SL) filtering method and decomposed into three different frequency bands (alpha, beta and gamma) using Discrete Wavelet Transform (DWT). We have used “db4” wavelet function for deriving a set of conventional and modified energy based features from the EEG signals for classifying emotions. Two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) methods are used and their performances are compared for emotional states classification. The experimental results indicate that, one of the proposed features (ALREE) gives the maximum average classification rate of 83.26% using KNN and 75.21% using LDA compared to those of conventional features. Finally, we present the average classification rate and subsets of emotions classification rate of these two different classifiers for justifying the performance of our emotion recognition system.