Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review

被引：15
作者：
Oubrahim, Zakarya [1 ]
Amirat, Yassine [2 ]
Benbouzid, Mohamed [3 ,4 ]
Ouassaid, Mohammed [1 ]
机构：
[1] Mohammed V Univ Rabat, Engn Smart & Sustainable Syst Res Ctr, Mohammadia Sch Engineers, Rabat 10090, Morocco
[2] LbISEN, ISEN Yncrea Ouest, F-29200 Brest, France
[3] Univ Brest, Inst Rech Dupuy Lome UMR CNRS 6027, F-29238 Brest, France
[4] Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China
来源：
ENERGIES | 2023年 / 16卷 / 06期
关键词：
smart grid; power quality monitoring; disturbances characterization; detection; estimation; classification; signal processing methods; pattern recognition methods; information theoretical criteria; phasor measurement unit (PMU); EMPIRICAL-MODE DECOMPOSITION; OPTIMAL FEATURE-SELECTION; HILBERT-HUANG TRANSFORM; MULTIRESOLUTION S-TRANSFORM; WAVELET PACKET ENERGY; AUTOMATIC CLASSIFICATION; FEATURE-EXTRACTION; ARTIFICIAL-INTELLIGENCE; FOURIER-TRANSFORM; NEURAL-NETWORKS;
D O I：
10.3390/en16062685
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes.
引用
收藏
页数：41
相关论文
共 239 条
[1]   Classification of power system disturbances using linear Kalman filter and fuzzy-expert system [J].
Abdelsalam, Abdelazeem A. ;
Eldesouky, Azza A. ;
Sallam, Abdelhay A. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2012, 43 (01) :688-695
[2]   Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System [J].
Achlerkar, Pankaj D. ;
Samantaray, S. R. ;
Manikandan, M. Sabarimalai .
IEEE TRANSACTIONS ON SMART GRID, 2018, 9 (04) :3122-3132
[3]   An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances [J].
Ahila, R. ;
Sadasivam, V. ;
Manimala, K. .
APPLIED SOFT COMPUTING, 2015, 32 :23-37
[4]   A genetic based algorithm for voltage flicker measurement [J].
Al-Hasawi, WM ;
El-Naggar, KM .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2004, 26 (08) :593-596
[5]   A Review of Machine Learning Approaches to Power System Security and Stability [J].
Alimi, Oyeniyi Akeem ;
Ouahada, Khmaies ;
Abu-Mahfouz, Adnan M. .
IEEE ACCESS, 2020, 8 :113512-113531
[6]  
Amirat Y, 2015, PROC IEEE INT SYMP, P1351, DOI 10.1109/ISIE.2015.7281669
[7]  
[Anonymous], 2005, C371182005 IEEE
[8]  
[Anonymous], 1986, Introduction to expert systems
[9]  
[Anonymous], 2008, Proceedings of the 2nd International Conference on Heterogeneous Material Mechanics, Huangshan City, China
[10]  
[Anonymous], 2011, IEEE Std C37.118.1a-2014 Amendment to IEEE Std C37.118.1-2011, P1, DOI [DOI 10.1109/IEEESTD.2011.6111219, 10.1109/IEEESTD.2011.6111219]
12345678910