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ANFIS Approach for Noise Reduction of Lightning Current Online Monitoring System

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
ANFIS – nowa metoda redukcji szumów w systemach monitorowania prądu wyładowań
Języki publikacji
EN
Abstrakty
EN
A novel de-noising algorithm, based on adaptive neural-fuzzy inference system (ANFIS) is proposed for noise reduction of the lightning current online monitoring system. The paper presents the theory and the implement procedure of the fuzzy neural system. Comparisons among the traditional strategies, such as curve fitting (CF), wavelet transform (WT) methods and the proposed ANFIS strategy are carried out. The simulation results demonstrate the superiority of the proposed method. Moreover, the employed approach has been tested on the practical measured current of lightning current online monitoring system. The testing results validate the proposed approach.
PL
Zaproponowano nowy algorytm odszumiania bazujący na adaptacyjnym neuro-fuzzy systemie interferencji ANFIS. System zastosowano przy monitorowaniu prądu wyładowań. System porównano z innymi dotychczas stosowanymi – dopasowanie krzywej czy transformata falkowa.
Rocznik
Strony
108--112
Opis fizyczny
Bibliogr. 18 poz., schem., tab., wykr
Twórcy
autor
autor
autor
  • High Voltage Center of Shanghai Jiao Tong University, No.1954, Huashan Road, Shanghai, 200030, China, nnyan@sjtu.edu.cn
Bibliografia
  • [1] Fangxing Li, Wei Qiao, Hongbin Sun, et.al., Smart Transmission Grid: Vision and Framework, IEEE Transaction on Smart Grid, 1(2010), No. 2, 168-177
  • [2] Chen Haibo, Wang Cheng, Li Junfeng, et.al., Application of Online Monitoring Technologies for UHV AC Transmission Lines, Power System Technology, 33(2009), No. 10, 55-58
  • [3] Chen Jiahong, Zhang Qin, Feng Wanxing, et.al., Lightning Location System and Lightning Detection Network of China Power Grid, High Voltage Engineering, 34(2008), No. 3, 425-431
  • [4] Bojie Sheng, Wenjun Zhou, Ultra-low Power Wireless-Online-Monitoring Platform for Transmission Line in Smart Grid, 2010 Int. Conf. on High Voltage Engineering and Application, 2010, 244-247
  • [5] Lu Junjie, Wang Jufeng, Peng Yuning, et.al., Lightning Strike Monitoring System of Overhead Transmission Lines, Electric Power Automation Equipment, 30(2010), No. 1, 132-135
  • [6] P. Liatos, A.M. Hussein, Characterization of 100-kHz Noise in the Lightning Current Derivative Signals Measured at the CN Tower, IEEE Transaction on Electromagnetic Compatibility, 47(2005), No. 4, 986-997
  • [7] S. V. Chandrashekhar Aiya, Noise Power Radiated by Tropical Thunderstorms, Proceedings of the IRE, 43(1955), No.8, 966-974
  • [8] Ouarda Nedjah, A.M. Hussein, R. Sotudeh, et.al., Wavelet Noise Removal from CN Tower Lightning Current Waveforms”, International Signal Processing Conference, 2003, Paper 505, 1-6
  • [9] Gamacho F, Aro M, Schon K, et.al., Evaluation Procedures for Lightning Impulse Parameters in Case of Waveforms with Oscillations and/ or an Over Shoot, IEEE Transactions on Power Delivery, 12(1997), No. 2, 640-649
  • [10] Nedjah, O., Hussein, A.M., Krishnan, S., et. al., A Divide-and- Conquer Approach for Denoising and Modeling the CN Tower Lightning Current Derivative Signal, Canadian Conference on Electrical and Computer Engineering, 2008, 001373-001378.
  • [11] F. Heidler, J.M. Cvetic, B.V. Stanic, Calculation of Lightning Current Parameters, IEEE transaction on Power delivery, 14(1999), No. 2, 399-404
  • [12] Ren Shunping, Luo Fushan, Zhuang Hongchun, et.al., A Neural Network Based On-line Process of Measured Data of Balloonborn Two Sphere Electric Field Instrument, Power System Technology, 25(2001), No. 7, 41-43
  • [13] Zhai Donghai, Li Li, Non-linear Noise Canceller Based on Additive-multiplicative Fuzzy Neural Network, Journal of Southwest Jiaotong University, 39(2004), No. 1, 112-116
  • [14] Zhang Bin, Zhang Donglai, Parametric Compression Algorithm for Power System Steady Data, Proceedings of the CSEE, 31(2011), No. 1, 72-79
  • [15] McComb T R, Lagnese J E, Calculating the Parameters of Full Lightning Impulses Using Model Based Curve Fitting, IEEE Transactions on Power Delivery, 6(1991), No. 4, 1386-1394
  • [16] Suna Bolat, Özcan Kalenderli, De-noising of Lightning Impulse Voltage Waveform Using Wavelet Transform, IEEE 15th. Conf. on Signal Processing and Communications Applications, 2007, 1-4
  • [17] Jyh-Shing Roger Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transaction on Systems, Man, and Cybernetics, 23(1993), No. 3, 665-685
  • [18] Yang Lu, Yang Haitao, Shen Huairong, The Application of ANFIS and WT in Filtering, 2010 2nd Int. Conf. on Information Engineering and Computer Science, 2010, 1-3
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOH-0065-0023
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