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

Embedded Parallel Computing Platform for Real-Time Recognition of Power Quality Disturbance Based on Deep Network

Systems powered by scattered sustainable power sources are highly susceptible to disturbances in the quality of power. Power Quality Disturbances (PQD) signals can degrade the functionality of grid-powered appliances. The older techniques for recognizing thePQD signals involve feature extraction. Manual analysis needs to set up a digital signal processor platform, which may lead to a time-complex process and errors in accuracy. Real-time PQD (RPQD) recognition techniques have advanced Embedded Parallel Computing Platform(EPCP), various signal processing methods, artificial intelligence, and Deep Network (DN) methodologies to recognize RPQD signals successfully in real-time scenarios using EPCP-RPQD-DN. Initially, the proposed algorithm implements hybridized Deep Belief Network and Long Short-Term Memory (DBN-LSTM) to accurately recognize the real-time PQD signals. Secondly, the DBN module maximizes the input signal features for the generation of PQD in a fixed period by training phase directly from raw PQD input signals and forwards it to the LSTM module. Third, in LSTM, the time series nature of PQD signals is easily analyzed using three layers, allowing it to run on the EPCP model. The PQD sample signals are employed to train the DBN in a central monitoring server. A series of PQD signals generated by the EPCP simulation environment is carried out to validate the effectiveness of the EPCP-RPQD-DN approach. Real-time simulation of electromagnetic fault conditions in the power system by Real Time Digital Simulator (RTDS) hardware. Experimental evaluation shows that DN learning improves accuracy rate, reduces computational overhead, and minimizes error rate compared to existing approaches

Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network

Power Quality Management and Classification for Smart Grid Application using Machine Learning

The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development

Generalized signal-dependent noise model and parameter estimation for natural images

International audienceThe goal of this paper is to propose a generalized signal-dependent noise model that is more appropriate to describe a natural image acquired by a digital camera than the conventional Additive White Gaussian Noise model widely used in image processing.This non-linear noise model takes into account effects in the image acquisition pipeline of a digital camera. In this paper, an algorithm for estimation of noise model parameters from a single image is designed. Then the proposed noise model is applied with the Local Linear Minimum Mean Square Error filter to design an efficient image denoising method