A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

Abstract

Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its preeminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.

Digital Modeling System for Dynamic Nonlinear Systems of Power Equipment in Digital Grids and 5G

“…A technical term for this process is grid-edge control, and it refers to the control of distributed energy resources at the grid edges that makes use of different data resources that have been created by the digital transformation [38]. Microgrids are becoming more and more commonplace in the United States [39]. The term "microgrid" refers to a system made up of distributed energy resources that are electrically connected and coordinated to serve as self-contained energy sources that can be connected to the utility grid or run independently in the context of energy [9].…”

Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

“…The kernel method represented by the intelligent sensor network has brought the third revolution in the field of pattern analysis. This kind of method implicitly maps the original spatial data to the feature space through the kernel function [3][4][5], finds a linear relationship in the feature space, and realizes the efficient solution of nonlinear problems [6]. The failures of subway machinery often show nonlinear behavior [7], and the kernel method is especially suitable for the processing of subway machinery fault diagnosis and mode analysis problems [8].…”

A Survey of Preprocessing Methods Used for Analysis of Big Data Originated From Smart Grids

“…In conclusion, the AI applications can improve and enhance the resilience and reliability of intelligent power grid platforms. The researchers in [29] present a complete review for detecting and classifying power quality disturbances by elaborating AI tools and signal processing methods with their cons and pros. Moreover, automatic recognition methods are critically analyzed for different modes of operations, AI methods, FS methods, preprocessing tools and the energy input signal types (noisy/real/synthetic).…”