There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Requirement of green supply with higher quality has been consumers’ demand around the globe The electrical power system is expected to deliver undistorted sinusoidal rated voltage and current continuously at rated frequency to the consumers This paper presents a comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances It is intended to provide a wide spectrum on the status of detection and classification of PQ disturbances as well as an effect of noise on detection and classification of PQ events to the researchers, designers and engineers working on power quality More than 150 research publications on detection and classification techniques of PQ disturbances have been critically examined, classified and listed for quick reference

A critical review of detection and classification of power quality events

The detection and classification of power quality (PQ) disturbances have become a pressing concern due to the increasing number of disturbing loads connected to the power line and the susceptibility of certain loads to the presence of these disturbances; moreover, they can appear simultaneously since, in any real power system, there are multiple sources of different disturbances. In this paper, a new dual neural-network-based methodology to detect and classify single and combined PQ disturbances is proposed, consisting, on the one hand, of an adaptive linear network for harmonic and interharmonic estimation that allows computing the root-mean-square voltage and total harmonic distortion indices. With these indices, it is possible to detect and classify sags, swells, outages, and harmonics-interharmonics. On the other hand, a feedforward neural network for pattern recognition using the horizontal and vertical histograms of a specific voltage waveform can classify spikes, notching, flicker, and oscillatory transients. The combination of the aforementioned neural networks allows the detection and classification of all the aforementioned disturbances even when they appear simultaneously. An experiment under real operating conditions is carried out in order to test the proposed methodology.

Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks

The increasing trend towards renewable energy sources requires higher power quality (PQ) at the generation, transmission and distribution systems. The PQ disturbances are produced due to the nonlinear loads, power electronic converters, system faults and switching events. The utilities and consumers of electric power are expected to acquire ideal voltage and current waveforms at rated power frequency. The development of new techniques for the automatic classification of PQ events is at present a major concern. This paper presents a comprehensive literature review on the applications of digital signal processing, artificial intelligence and optimization techniques in the classification of PQ disturbances. Various signal processing techniques used for the feature extraction such as Fourier transform, wavelet transform, S-transform, Hilbert transform, Gabor transform and their hybrids have been reviewed. The artificial intelligent techniques used for the pattern recognition such as artificial neural network, fuzzy logic, support vector machine are reviewed in detail. The optimization techniques used for the optimal feature selection such as genetic algorithm, particle swarm optimization and ant colony optimization are also reviewed. A comparison of various classification systems is presented in tabular form which highlights the important techniques used in the field of PQ disturbance monitoring. The comparison of research works carried out on the classification of PQ disturbances points out that many researchers have focussed on the feature extraction and classification techniques. Only few authors have used the feature selection techniques for selecting the best suitable features. This review may be considered a valuable source for researchers as a reference point to explore the opportunities for further improvement in the field of PQ classification.

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Classification of power quality events – A review

This paper proposes a new algorithm based on the wavelet-packet transform for the analysis of harmonics in power systems. The proposed algorithm decomposes the voltage/current waveforms into the uniform frequency bands corresponding to the odd-harmonic components of the signal and uses a method to reduce the spectral leakage due to the imperfect frequency response of the used wavelet filter bank. This paper studies the selection of the mother wavelet, the sampling frequency, and the frequency characteristics of the wavelet filter bank for the two most common wavelet functions used for harmonic analysis and compares the performance of the proposed method with the results obtained using the discrete Fourier transform (DFT) analysis and the harmonic-group concept introduced by the International Electrotechnical Commission (IEC) under different measurement conditions.

Analysis of Harmonics in Power Systems Using the Wavelet-Packet Transform

This paper presents a method to estimate the harmonic groups in voltage and current waveforms using the wavelet-packet transform. The wavelet-packet decomposition algorithm is selected to obtain output bands compatible with the harmonic groups defined in the harmonics measurement standards for power-supply systems. The method proposed is analyzed in the measurement of harmonic components, either synchronized or nonsynchronized, with the fundamental power frequency, comparing the results with the method proposed in the international standards.

Application of the wavelet-packet transform to the estimation of harmonic groups in current and voltage waveforms

The paper proposes an algorithm based on the wavelet-packet transform for the analysis of harmonics in power systems. The algorithm proposed decomposes the voltage/current waveforms into uniform frequency bands corresponding to the odd harmonic components of the signal. First, the selection of the mother wavelet, the sampling frequency and the frequency characteristics of the selected wavelet filter bank are studied. Finally the performance of the method proposed is compared with the results obtained using DFT analysis and the harmonic group concept introduced by the International Electrotechnical Commission using different test waveforms proposed in standard IEC-61000-4-7

Analysis of Harmonics in Power Systems Using the Wavelet Packet Transform

This paper presents an automatic system for real-time detection and analysis of voltage events in power systems. The voltage events considered are those that cause a temporary increase or decrease in the rms voltage magnitude over the limits recommended in the international standards. The system proposed uses three Kalman filters to detect when a voltage event begins and to estimate the three-phase voltage supply during the event. The results obtained from real measurements in a low-voltage distribution network show that the system accurately detects and analyzes in real time different types of voltage events in power systems

Julio Barros

Papers

Analysis of Harmonics in Power Systems Using the Wavelet-Packet Transform

Application of the wavelet-packet transform to the estimation of harmonic groups in current and voltage waveforms

Analysis of Harmonics in Power Systems Using the Wavelet Packet Transform

Automatic Detection and Analysis of Voltage Events in Power Systems

Applications of wavelets in electric power quality: Voltage events