Improved Disturbance Detection Technique for Power-Quality Analysis

被引：14

作者：

Gomes Marques, Cristiano Augusto ^{[1
]}

Ferreira, Danton Diego ^{[2
]}

Freitas, Lucas Romero ^{[3
]}

Duque, Carlos Augusto ^{[3
]}

Ribeiro, Moises Vidal ^{[3
]}

机构：

[1] Univ Fed Rio de Janeiro, COPPE, BR-21941972 Rio De Janeiro, Brazil

[2] Univ Fed Lavras, BR-37200000 Lavras, MG, Brazil

[3] Univ Fed Juiz de Fora, Dept Elect Engn, BR-36036330 Juiz De Fora, MG, Brazil

来源：

IEEE TRANSACTIONS ON POWER DELIVERY | 2011年 / 26卷 / 02期

关键词：

Disturbance detection; higher order statistics; power quality (PQ);

D O I：

10.1109/TPWRD.2010.2092571

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents an improved technique for detecting disturbances in voltage signals for power quality analysis. The main advantage of the proposed technique lies in its capability to detect disturbances when power frequency is time-varying. In addition, the technique is capable of detecting disturbances in frames corresponding to 1/64 of the fundamental component. Simulation results indicate that the proposed technique can offer improved performance in comparison with previous one.

引用

页码：1286 / 1287

页数：2

共 50 条

[11] Adaptive wavelet networks for power-quality detection and discrimination in a power system
Lin, Chia-Hung
Wang, Chia-Hao
[J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2006, 21 (03) : 1106 - 1113
[12] Mitigation of Power Quality Problems Using Unified Power Quality Conditioner by an Improved Disturbance Extraction Technique
Alawadhi, Nabecl
Elnady, A.
[J]. 2017 INTERNATIONAL CONFERENCE ON ELECTRICAL AND COMPUTING TECHNOLOGIES AND APPLICATIONS (ICECTA), 2017, : 225 - 229
[13] Wavelet-based fuzzy reasoning approach to power-quality disturbance recognition
Zhu, TX
Tso, SK
Lo, KL
[J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2004, 19 (04) : 1928 - 1935
[14] A new power quality disturbance detection method based on the improved LMD
Song, Haijun
Huang, Chuanjin
Liu, Hongchao
Chen, Tiejun
Li, Jingli
Luo, Yong
[J]. Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2014, 34 (10): : 1700 - 1708
[15] An Improved Power Quality Disturbance Detection Using Deep Learning Approach
Sekar, Kavaskar
Kanagarathinam, Karthick
Subramanian, Sendilkumar
Venugopal, Ellappan
Udayakumar, C.
[J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
[16] A Novel Reference Signal Generation Method for Power-Quality Improvement of Unified Power-Quality Conditioner
Teke, Ahmet
Saribulut, Lutfu
Tumay, Mehmet
[J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2011, 26 (04) : 2205 - 2214
[17] Adaptive Prony Method for the Calculation of Power-Quality Indices in the Presence of Nonstationary Disturbance Waveforms
Andreotti, A.
Bracale, A.
Caramia, P.
Carpinelli, G.
[J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2009, 24 (02) : 874 - 883
[18] Power Quality Disturbance Detection Based on Improved Robust Random Cut Forest
Zhang, Ge
Bai, Feifei
Cui, Yi
Dart, David
Yaghoobi, Jalil
Zillmann, Matthew
[J]. 2022 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (I&CPS ASIA 2022), 2022, : 1697 - 1702
[19] Application of Improved LMD Algorithm in Signal Detection of Power Quality Disturbance in Microgrid
Xu Y.
Gao Y.
Li Z.
Xi L.
[J]. Dianwang Jishu/Power System Technology, 2019, 43 (01): : 332 - 339
[20] Detection and classification of multiple power-quality disturbances with wavelet multiclass SVM
Lin, Whei-Min
Wu, Chien-Hsien
Lin, Chia-Hung
Cheng, Fu-Sheng
[J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2008, 23 (04) : 2575 - 2582

← 1 2 3 4 5 →