Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area

Automatic classification of power quality disturbances using optimal feature selection based algorithm

The development of renewable energy sources and power electronic converters in conventional power systems leads to Power Quality (PQ) disturbances. This research aims at automatic detection and classification of single and multiple PQ disturbances using a novel optimal feature selection based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). DWT is used for the extraction of useful features, which are used to distinguish among different PQ disturbances by an ANN classifier. The performance of the classifier solely depends on the feature vector used for the training. Therefore, this research is required for the constructive feature selection based classification system. In this study, an Artificial Bee Colony based Probabilistic Neural Network (ABCPNN) algorithm has been proposed for optimal feature selection. The most common types of single PQ disturbances include sag, swell, interruption, harmonics, oscillatory and impulsive transients, flicker, notch and spikes. Moreover, multiple disturbances consisting of combination of two disturbances are also considered. The DWT with multi-resolution analysis has been applied to decompose the PQ disturbance waveforms into detail and approximation coefficients at level eight using Daubechies wavelet family. Various types of statistical parameters of all the detail and approximation coefficients have been analysed for feature extraction, out of which the optimal features have been selected using ABC algorithm. The performance of the proposed algorithm has been analysed with different architectures of ANN such as multilayer perceptron and radial basis function neural network. The PNN has been found to be the most suitable classifier. The proposed algorithm is tested for both PQ disturbances obtained from the parametric equations and typical power distribution system models using MATLAB/Simulink and PSCAD/EMTDC. The PQ disturbances with uniformly distributed noise ranging from 20 to 50 dB have also been analysed. The experimental results show that the proposed ABC-PNN based approach is capable of efficiently eliminating unnecessary features to improve the accuracy and performance of the classifier

Insight on Effectiveness of Frequently Exercised PQ Classification Techniques

The growing demands of global consumer market in green energy system have opened the doors for many technologies as well as various sophisticated electrical devices for both commercial and domestic usage. However, with the increasing demands of energy and better quality of services, there is a significant increase in non-linearity in load distribution causing potential effect on the Power Quality (PQ). The harmful effects on PQ are various events e.g. sag, swell, harmonics etc that causes significant amount of system degradation. Therefore, this paper discusses various significant research techniques pertaining to the PQ disturbance classification system introduced by the authors in the past and analyzes its effectiveness scale in terms of research gap. The paper discusses some of the frequently exercised PQ classification techniques from the most relevant literatures in order to have more insights of the techniques

A parallel windowing approach to the Hough transform for line segment detection

In the wide range of image processing and computer vision problems, line segment detection has always been among the most critical headlines. Detection of primitives such as linear features and straight edges has diverse applications in many image understanding and perception tasks. The research presented in this dissertation is a contribution to the detection of straight-line segments by identifying the location of their endpoints within a two-dimensional digital image. The proposed method is based on a unique domain-crossing approach that takes both image and parameter domain information into consideration. First, the straight-line parameters, i.e. location and orientation, have been identified using an advanced Fourier-based Hough transform. As well as producing more accurate and robust detection of straight-lines, this method has been proven to have better efficiency in terms of computational time in comparison with the standard Hough transform. Second, for each straight-line a window-of-interest is designed in the image domain and the disturbance caused by the other neighbouring segments is removed to capture the Hough transform buttery of the target segment. In this way, for each straight-line a separate buttery is constructed. The boundary of the buttery wings are further smoothed and approximated by a curve fitting approach. Finally, segments endpoints were identified using buttery boundary points and the Hough transform peak. Experimental results on synthetic and real images have shown that the proposed method enjoys a superior performance compared with the existing similar representative works

Two-stream convolutional neural network for non-destructive subsurface defect detection via similarity comparison of lock-in thermography signals

Active infrared thermography is a safe, fast, and low-cost solution for subsurface defects inspection, providing quality control in many industrial production tasks. In this paper, we explore deep learning-based approaches to analyze lock-in thermography image sequences for non-destructive testing and evaluation (NDT&amp;E) of subsurface defects. Different from most existing Convolutional Neural Network (CNN) models that directly classify individual regions/pixels as defective and non-defective ones, we present a novel two-stream CNN architecture to extract/compare features in a pair of 1D thermal signal sequences for accurate classification/differentiation of defective and non-defective regions. In this manner, we can significantly increase the size of the training data by pairing two individually captured 1D thermal signals, thereby greatly easing the requirement for collecting a large number of thermal sequences of specimens with defects to train deep CNN models. Moreover, we experimentally investigate a number of network alternatives, identifying the optimal fusion scheme/stage for differentiating the thermal behaviors of defective and non-defective regions. Experimental results demonstrate that our proposed method, directly learning how to construct feature representations from a large number of real-captured thermal signal pairs, outperforms the well-established lock-in thermography data processing techniques on specimens made of different materials and at various excitation frequencies.</p