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

As one step toward the future smart grid, condition monitoring is important to facilitate the reliability of grid asset operation and to save on maintenance cost [1]. Most failures of the power grid are caused by electrical insulation failure, and a key indicator of such electrical failure is the occurrence of partial discharge (PD). Therefore, one focus of condition monitoring is to detect PD, especially in the early stages, to prevent a serious power failure or outage.

An overview of state-of-the-art partial discharge analysis techniques for condition monitoring

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

Partial discharge classifications: Review of recent progress

In extracting partial discharge (PD) signals embedded in excessive noise, the need for an online and automated tool becomes a crucial necessity One of the recent approaches that have gained some acceptance within the research arena is the Wavelet multi- resolution analysis (WMRA) However selecting an accurate mother wavelet, defining dynamic threshold values and identifying the resolution levels to be considered in the PD extraction from the noise are still challenging tasks This paper proposes a novel wavelet-based technique for extracting PD signals embedded in high noise levels The proposed technique enhances the WMRA by decomposing the noisy data into different resolution levels while sliding it into Kaiser's window Only the maximum expansion coefficients at each resolution level are used in de-noising and measuring the extracted PD signal A small set of coefficients is used in the monitoring process without assigning threshold values or performing signal reconstruction The proposed monitoring technique has been applied to a laboratory data as well as to a simulated PD pulses embedded in a collected laboratory noise

On-line detection and measurement of partial discharge signals in a noisy environment

This paper presents a novel approach to the classification of power quality disturbances by the employment of Hidden Markov Models. In these models, power quality disturbances are represented by a sequence of consecutive frames. Both the Fourier and Wavelet Transforms are utilized to produce sequence of spectral vectors that can accurately capture the salient characteristics of each disturbance. Vector Quantization is used to assign chain of labels for power quality disturbances utilizing their spectral vectors. From these labels, a separate Hidden Markov Model is developed for each class of the power quality disturbances in the training phase. During the testing stage, the unrecognized disturbance sequence is matched against all the developed Hidden Markov Models. The best-matched model pinpoints the class of the unknown disturbance. Simulation results prove the competence of the proposed algorithm.

Disturbance classification using Hidden Markov Models and vector quantization

Electrical and acoustic partial discharge (PD) measurement and pattern recognition procedures are described for detecting and identifying contaminating particles in transformer mineral oils. This work introduces the use of support vector machines (SVM), a nonlinear non-parametric automatable machine learning algorithm, for the purpose of classifying the size and composition of such particles. The training and validation of acoustic and electrical PD measurement data, which are contaminated by time varying noise, are first filtered adaptively using wavelet decomposition. Statistics of a particle's impact upon collision with the walls of a tank, containing the electrode test assembly and the inter arrival time between collisions constitute the features for the SVM classifier. These statistics include higher order moments and the entropy of the estimated density function of the features. Results based on experimental training and testing data indicate that fusing of the acoustic and electric PD information at the features level provides a nearly perfect classification success rate. These observations demonstrate that, while electrical and acoustic PD data are correlated, they contain individually independent and complementary information regarding the state and condition of transformer type mineral oils.

SVM classification of contaminating particles in liquid dielectrics using higher order statistics of electrical and acoustic PD measurements

This letter presents a new technique to identify partial discharge locations in transformer winding using electrical measurements. Useful features, which pinpoint the locations of partial discharges, are extracted from the periodogram of the secondary voltage of the transformer. These features are manipulated by a distance classifier to determine the locations of the partial discharges. Simulation results proof that the proposed method is efficient and can handle effectively transformer parameters deviations due to aging.

T.K. Abdel-Galil

Papers

On-line detection and measurement of partial discharge signals in a noisy environment

Disturbance classification using Hidden Markov Models and vector quantization

SVM classification of contaminating particles in liquid dielectrics using higher order statistics of electrical and acoustic PD measurements

Identification of partial discharge locations in transformer winding using PSD estimation