An adaptive fault line selection method based on atomic comprehensive measure values for distribution network
Xiangxiang Wei
School of Electrical and Information Engineering, China Agricultural University, Beijing, 100083 China
Search for more papers by this authorCorresponding Author
Dechang Yang
School of Electrical and Information Engineering, China Agricultural University, Beijing, 100083 China
Correspondence
Dechang Yang, School of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.
Email: [email protected]
Search for more papers by this authorBoying Wen
School of Electrical and Information Engineering, China Agricultural University, Beijing, 100083 China
Search for more papers by this authorXiaowei Wang
School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shanxi, 710049 China
Search for more papers by this authorChenhui Yin
School of Electrical and Information Engineering, China Agricultural University, Beijing, 100083 China
Search for more papers by this authorJie Gao
College of Automation Engineering, Shanghai University of Electric Power, Shanghai, 200090 China
Search for more papers by this authorXiangxiang Wei
School of Electrical and Information Engineering, China Agricultural University, Beijing, 100083 China
Search for more papers by this authorCorresponding Author
Dechang Yang
School of Electrical and Information Engineering, China Agricultural University, Beijing, 100083 China
Correspondence
Dechang Yang, School of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.
Email: [email protected]
Search for more papers by this authorBoying Wen
School of Electrical and Information Engineering, China Agricultural University, Beijing, 100083 China
Search for more papers by this authorXiaowei Wang
School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shanxi, 710049 China
Search for more papers by this authorChenhui Yin
School of Electrical and Information Engineering, China Agricultural University, Beijing, 100083 China
Search for more papers by this authorJie Gao
College of Automation Engineering, Shanghai University of Electric Power, Shanghai, 200090 China
Search for more papers by this authorSummary
In this article, an adaptive fault line selection method based on atomic comprehensive measures for a distribution network is proposed. Firstly, wavelet denoising is introduced to remove the noise contained in the transient zero-sequence current; then, the matching pursuit algorithm is utilized to decompose the denoising transient zero-sequence current. Moreover, based on singular value decomposition and information entropy, the atomic singular entropy values are computed, and the actual energy of feature atoms are obtained. Furthermore, according to the fuzzy mathematics, the fault comprehensive measurement function is built. Finally, the line that has the maximum of the fault comprehensive measure value is chosen as the fault line. A large number of simulation experiments and comparing results proved that the proposed method has high selection accuracy and reliability, as well as a powerful ability to resist noise.
REFERENCES
- 1Zhang Z, Liu X, Piao Z. Fault line detection in neutral point ineffectively grounding power system based on phase-locked loop. IET Gener Transm Distribution. 2014; 8(2): 273–280.
- 2Yusuff A, Jimoh A, Munda J. Determinant-based feature extraction for fault detection and classification for power transmission lines. IET Gener Transm Distribution. 2011; 5(12): 1259–1267.
- 3Lin X, Sun J, Kursan I, et al. Zero-sequence compensated admittance based faulty feeder selection algorithm used for distribution network with neutral grounding through Peterson-coil. Int J Electr Power Energy Syst. 2014; 63(63): 747–752.
- 4Morais A, Júnior G, Mariotto L, Marchesan G. A morphological filtering algorithm for fault detection in transmission lines during power swings. Electr Power Syst Res. 2015; 122(4): 10–18.
- 5Fan S, Xu B, Zhang Q. A new method for fault line selection in distribution system with arc suppression coil grounding with square-wave signal injection. Autom Electr Power Syst. 2012; 36(4): 91–95.
- 6Flavio B. Fault-induced transient detection based on real-time analysis of the wavelet coefficient energy. IEEE Trans Power Del. 2014; 29(1): 140–153.
- 7Shu H. Unreal grounding identification and comprehensive line selection for resonant grounding system. Electr Power Autom Equip. 2016; 36(6): 122–129.
- 8Moslem D, Mohammad H, Taher N. Fast fault detection and classification based on a combination of wavelet singular entropy theory and fuzzy logic in distribution lines in the presence of distributed generations. Int J Electr Power Energy Syst. 2016; 78: 455–462.
- 9Shu H, Peng S. A fault line detection algorithm for distribution network of overhead line and underground cable mixed lines using S-transform energy from short window data. Trans China Electrotech Soc. 2009; 24(10): 153–160.
- 10Jin T, Chu F. A fault line-selection method in new distribution network with DG based on transient non-power frequency zero sequence current. Trans China Electrotech Soc. 2015; 30(17): 96–105.
- 11Ren Z, Zhang Y. Fault line selection of distribution network based on improved Hilbert-Huang transform and identification confidence degree. Power Syst Prot Control. 2015; 43(10): 8–13.
- 12Wu L, Huang C, Lin D, Zhu Z, Jiang H. Fault line selection based on transient wavelet energy for non-solid-earthed network. Electr Power Autom Equip. 2013; 33(5): 70–75.
- 13Jia Q, Shi L, Wang N, Dong H. A fusion method for ground fault line detection in compensated power networks based on evidence theory and information entropy. Trans China Electrotech Soc. 2012; 27(6): 191–197.
- 14Behnam M, Jian G. Fault classification and faulted phase selection based on the symmetrical components of reactive power for single-circuit transmission lines. IEEE Trans Power Del. 2013; 28(28): 2326–2332.
- 15Xue Y, Li J, Xu B. Transient equivalent circuit and transient analysis of single-phase earth fault in arc suppression coil grounded system. Proc CSEE. 2015; 35(22): 5703–5714.
- 16Emigdio Z, Leonardo T, Arturo S. Regularity and matching pursuit feature extraction for the detection of epileptic seizures. J Neurosci Methods. 2016; 266(15): 107–125.
- 17Li X, Gong Q, Guan Q, Zhang Y, Jia J. PSO based modal atom method and its application to track time-varying performance of low frequency oscillation modes. Proc CSEE. 2013; 33(10): 79–88.
- 18Cui M, Sun Y, Ke D, Wang S. Short-term wind power forecasting based on atomic sparse decomposition theory. Electr Power Autom Equip. 2014; 34(1): 120–126.
- 19Guodong L, Linhua H. Research on wavelet-based sparse representation of insulator leakage current signal. Int Trans Electr Energy Syst. 2013; 25(1): 320–329.
- 20Liu G, Victor D. Matching pursuits may yield superior results to orthogonal matching pursuits when secondary information is estimated from the signal model. IEEE Conference Publications. 2010; 2013-2016.
- 21Guodong L, Lihuang H. Research on wavelet-based sparse representation of insulator leakage current signal. Int Trans Electr Energy Syst. 2015; 25(1): 72–88.
- 22Nadakuditi R. An algorithm for improved low-rank signal matrix denoising by optimal data-driven singular value shrinkage. IEEE Trans Inf Theory. 2014; 60(5): 3002–3018.
- 23Dohyun K, Bangrae L, Hyuck J, Sang P, Yeongho M, Myong K. Automated detection of influential patents using singular values. IEEE Trans Autom Sci Eng. 2012; 9(4): 723–733.
- 24Saffet A. Effects of algebraic singularities on the voltage dynamics of differential-algebraic power system model. Eur Trans Electr Power. 2008; 18(6): 547–561.
- 25Zhao Y, Gao L, Wang Y, Peng M. A method to recognize fault symbol for adaptive single-phase reclosure based on energy entropy of singular value from S-transform. Power Syst Technol. 2010; 34(12): 210–214.
- 26Cabal-Yepez E, Fernandez-Jaramillo A, Garcia-Perez A, Romero-Troncoso R, Lozano-Garciat J. Real-time condition monitoring on VSD-fed induction motors through statistical analysis and synchronous speed observation. Int Trans Electr Energy Syst. 2015; 25(8): 1657–1672.
- 27He Z, Gao S, Chen X, Zhang J, Bo Z, Qian Q. Study of a new method for power system transients classification based on wavelet entropy and neural network. Int J Electr Power Energy Syst. 2011; 33(3): 402–410.
- 28Daqing M, Jingjun M. Discussion of fuzzy comprehensive evaluation application on earth fault line selection. Relay. 2007; 35(13): 1–6.
- 29Shu H, Cao P, Yang J, Dong J, Tian X. A method to distinguish between fault and lightning disturbance on transmission lines based on CVT secondary voltage and CT secondary current. Trans China Electrotech Soc. 2015; 30(3): 1–12.
- 30Foad S, Behnam M. Identification of inter-area oscillations using wavelet transform and phasor measurement unit data. Int Trans Electr Energy Syst. 2015; 25(11): 2831–2846.
- 31Wang Y, Dong Y, Liu X. Study on higher order wavelet packet singular entropy and its application to faulty line selection. Power Syst Protect Control. 2011; 39(8): 23–27.
- 32Zhigang L, Xiao G, Zongming X. The multi-core parallel algorithms of wavelet/wavelet packet transforms and their applications in power system harmonic analysis and data compression. Int Trans Electr Energy Syst. 2015; 25(11): 2800–2818.
- 33Wang X, Wei X, Gao J, Hou Y, Wei Y. Stepped fault line selection method based on spectral kurtosis and relative energy entropy of small current to ground system. J Appl Math. 2014; 2014(2): 1–18.
- 34Dan Z, Shen L, He Z. Entropy-involved energy measure study of intrinsic thermo acoustic oscillations. Appl Energy. 2016; 177(1): 570–578.
- 35Liu Y, Wang J, Ma J, Luo R. Comprehensive fault line selection method for resonant grounded system combining wavelet packet transform with fifth harmonic method. High Voltage Eng. 2015; 41(5): 1519–1525.
