A moving window average method for internal fault detection of power transformers

被引:5
|
作者
Taheri, Behrooz [1 ]
Sedighizadeh, Mostafa [2 ]
机构
[1] Islamic Azad Univ, Qazvin Branch, Fac Elect Biomed & Mechatron Engn, Qazvin, Iran
[2] Shahid Beheshti Univ, Fac Elect Engn, Tehran, Iran
来源
关键词
Inrush current; Differential relay; Power systems protection; Moving window averaging (MWA);
D O I
10.1016/j.clet.2021.100195
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Power transformers are among the mos]t critical components of power systems and should receive reliable protection against faults. Differential relays are widely used in the protection of power transformers. However, these relays are susceptible to mistaking the inrush currents, which are generated during the turning on of transformers. This paper presents a new method for detecting inrush currents based on the moving window averaging of current. The study found that using the Blackman Harris window rather than other windows in this method significantly reduces fault detection time. The proposed method is robust to strong noises such as white Gaussian noise. Finally, the proposed method was compared with the methods commonly used in industrial protection relays. In this comparison, it was demonstrated that the method offers several advantages over conventional techniques, including the ability to detect faults with high second harmonic and under current transformer saturation and to function properly in modern transformers.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] A novel technique for internal fault detection of power transformers based on moving windows
    Abniki, Hassan
    Sanaye-Pasand, Majid
    INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2014, 24 (09): : 1263 - 1278
  • [2] Internal fault detection techniques for power transformers
    Yadaiah, Narri
    Ravi, Nagireddy
    APPLIED SOFT COMPUTING, 2011, 11 (08) : 5259 - 5269
  • [3] Fault detection of power transformers using genetic programming method
    Zhang, Z
    Huang, WH
    Xiao, DM
    Liu, YL
    PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 3018 - 3022
  • [4] Detection of Inrush and Internal Fault in Power Transformers Based on Bacterial Foraging Optimization
    Gopila, M.
    Gnanambal, I.
    JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, 2017, 76 (01): : 32 - 37
  • [5] Fault detection techniques for power transformers
    Yadaiah, N.
    Ravi, Nagireddy
    2007 IEEE CONFERENCE ON INDUSTRIAL AND COMMERCIAL POWER SYSTEMS-TECHNICAL CONFERENCE, 2007, : 52 - +
  • [6] A New Method for Fault Detection and Identification of Incipient Faults in Power Transformers
    Ozgonenel, O.
    Kilic, Erdal
    Khan, M. Abdesh
    Rahman, M. Azizur
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2008, 36 (11) : 1226 - 1244
  • [7] Application of Parzen Window estimation for incipient fault diagnosis in power transformers
    Islam, Md Mominul
    Lee, Gareth
    Hettiwatte, Sujeewa Nilendra
    HIGH VOLTAGE, 2018, 3 (04): : 303 - 309
  • [8] Fault Diagnosis Method Based on Moving Window PCA
    Lu, Binzhang
    Zhao, Yuhong
    Mao, Zhenhua
    CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 185 - 188
  • [9] Internal Fault Discrimination in Power Transformers Using Curvelet Transform
    Ravikrishnan, G.
    Kiran, P.
    Kanakasabapathy, P.
    2013 4TH INTERNATIONAL YOUTH CONFERENCE ON ENERGY (IYCE), 2013,
  • [10] Neural Network Ensemble for Power Transformers Fault Detection
    Furundzic, Drasko
    Djurovic, Zeljko
    Celebic, Vladimir
    Salom, Iva
    ELEVENTH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING (NEUREL 2012), 2012,