Carlos A. Duque

Detection of disturbances in voltage signals for power quality analysis using HOS

This paper outlines a higher-order statistics (HOS)-based technique for detecting abnormal conditions in voltage signals. The main advantage introduced by the proposed technique refers to its capability to detect voltage disturbances and their start and end points in a frame whose length corresponds to, at least, samples or of the fundamental component if a sampling rate equal to Hz is considered. This feature allows the detection of disturbances in submultiples or multiples of one-cycle fundamental component if an appropriate sampling rate is considered. From the computational results, one can note that almost all abnormal and normal conditions are correctly detected if s256, 128, 64, 32, and 16 and the SNR is higher than 25 dB. In addition, the proposed technique is compared to a root mean square (rms)-based technique, which was recently developed to detect the presence of some voltage events as well as their sources in a frame whose length ranges from up to one-cycle fundamental component. The numerical results reveal that the proposed technique shows an improved performance when applied not only to synthetic data, but also to real one.

A PLL-Based Multirate Structure for Time-Varying Power Systems Harmonic/Interharmonic Estimation

This paper describes a phase-locked-loop (PLL)-based power systems harmonic estimation algorithm, which uses an analysis filter bank and multirate processing. The filter bank is composed of bandpass filters. The initial center frequency of each filter is purposely chosen to be equal to harmonic frequencies. However, an adaptation strategy makes it possible to track time-varying frequencies as well as interharmonic components. A downsampler device follows the filtering stage, reducing the computational burden, especially because undersampling operations are performed. Finally, the last stage is composed of a PLL estimator which provides estimates for amplitude, phase, and frequency of the input signal. The proposed method improves the accuracy, computational effort, and convergence time of the previous harmonic estimator based on cascade PLL configuration.

Power quality event detection based on the divide and conquer principle and innovation concept

The aim of this paper is to introduce a novel real-time digital signal processing technique for power quality disturbance detection. The proposed technique is inspired on the Divide and Conquer Principle and on the Innovation Concept. Essentially, the Divide and Conquer Principle is applied in splitting the monitored disturbance event into stationary and nonstationary components, allowing localized individual signal analysis. Thereafter, the Innovation Concept is introduced for revealing new information not previously observed in the energy of the nonstationary signals and so detecting a new event. In addition, this paper demonstrates a straightforward and feasible way to evaluate the Total Harmonic Distortion (THD) or a distortion measure for voltage signals. Simulation results indicate that the proposed technique is able to accurately detect a variety of voltage disturbances.

POWER SYSTEMS SIGNAL PROCESSING FOR SMART GRIDS

With special relation to smart grids, this book provides clear and comprehensive explanation of how Digital Signal Processing (DSP) and Computational Intelligence (CI) techniques can be applied to solve problems in the power system. Its unique coverage bridges the gap between DSP, electrical power and energy engineering systems, showing many different techniques applied to typical and expected system conditions with practical power system examples. Surveying all recent advances on DSP for power systems, this book enables engineers and researchers to understand the current state of the art and to develop new tools. It presents: - an overview on the power system and electric signals, with description of the basic concepts of DSP commonly found in power system problems - the application of several signal processing tools to problems, looking at power signal estimation and decomposition, pattern recognition techniques, detection of the power system signal variations

Transmission systems power quality monitors allocation

This work develops and tests a branch and bound algorithm for solving optimum allocation of power quality monitors in a transmission power system. The optimization problem is solved by using 0-1 integer programming techniques and depends highly on network topology. The algorithm, which is implemented in Matlab software, minimizes the total cost of the monitoring system and found the optimum number and locations for monitors on the network studied, under a set of given network observability constraints. Case studies are presented for IEEE test networks and for CEMIG actual transmission power systems. Current and voltage values are estimated by using monitored variables to validate the obtained results.

Novel method for tracking time-varying power harmonic distortions without frequency spillover

Although it is well known that Fourier analysis is in reality only accurately applicable to steady state waveforms, it is a widely used tool to study and monitor time-varying signals, which are commonplace in electrical power systems. The disadvantages of Fourier analysis, such as frequency spillover or problems due to sampling (data window) truncation can often be minimized by various windowing techniques, but they nevertheless exist. This paper demonstrates that it is possible to track and visualize amplitude and time-varying power systems harmonics, without frequency spillover caused by time-frequency techniques. This new tool allows for a clear visualization of time-varying harmonics which can lead to better ways to track harmonic distortion and understand time-dependent power quality parameters. It also has the potential to assist with control and protection applications.

An enhanced data compression method for applications in power quality analysis

This paper presents an enhanced method for data compression using a wavelet transform, to be applied in power systems signals for quality evaluation. The proposed approach is based on a previous estimation of the sinusoidal components of the signal under analysis, so that it could be subtracted from the original data in order to generate a transient type signal, which is subsequently applied to the compression techniques. The approach employs the Kalman filter and the adaptive notch filter techniques to provide the estimation of the sinusoidal components. Taking into account the wavelet property of sparse representation makes an improvement in the compression rate and in the signal degradation is attained. Finally, a proposed frame format to store the coded signal is presented.

A Controlled Filtering Method for Estimating Harmonics of Off-Nominal Frequencies

The increasing use of power electronics in power systems causes a high injection of harmonic components which can in turn interfere with utility equipment and customer loads. Therefore, the correct estimation and measurement of harmonics have become an important issue. If the power frequency of the signal is steady and near the nominal value, the discrete Fourier transform (DFT) can be used and good estimation performance is achieved. However, there are considerable power frequency variations on isolated systems such as shipboard power systems, micro-grids and smart-grids. When these variations occur there may be significant errors in the estimates using the DFT. In order to deal with this problem, this work presents a novel technique based on demodulation of the power line signal and subsequent filtering for harmonics estimation. The main features of the proposed harmonic estimation technique are: precise and accurate estimation of harmonics of off-nominal frequencies and fast estimation of harmonics (about two cycles of the fundamental component). Simulation results show that the proposed technique performs well in comparison with the DFT and can be a good candidate to replace it in cases where the power frequency is subject to considerable variations.

An improved method for signal processing and compression in power quality evaluation

This paper introduces a new waveform coding technique, based on wavelet transform, for power quality monitoring purposes. The proposed enhanced data compression method (EDCM) presents a complete adaptive signal processing approach to estimate the fundamental sinusoidal component and separate it from the transient ones in the monitored signal. When these nonstationary components are submitted to the compression technique, the sparse representation property of the wavelet transform provides an improvement in the compression ratio. Also, the degradation inserted by the lossy compression process is minimized. Simulation results confirm the effectiveness of the proposed method when compared to the standard solution, characterized by the compression of the whole monitored signal.

A New Method of Current-Transformer Saturation Detection in the Presence of Noise

The nonlinear characteristics of magnetization of a current transformer (CT) can cause waveform distortion in the secondary current, which may result in malfunction of protective relays. This phenomenon is known as CT saturation. The accurate saturation detection, by the use of digital signal processing techniques applied to secondary current, plays an important function in solving the problems caused by the saturation. This paper presents a new method of saturation detection, based on the first difference of the low noise Lanczos filter. The formulation of the adaptive threshold according to the level of the SNR is presented and the detection algorithm is implemented, tested and compared with the third difference function method. The tests were performed using distorted secondary current signals generated by the RTDS (Real Time Digital Simulator) and corrupted by several levels of White Gaussian Noise (WGN). The results showed a high detection capability of the proposed algorithm regardless the CT characteristics, the secondary burden and the fault characteristics. Additionally, the method showed to be robust against noise up to 40 dB SNR and suitable for real-time implementation.