人工智能在配电网高阻接地故障检测中的应用及展望
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摘要
配电网发生高阻接地故障时,接地电阻较大并伴随着电弧熄灭与重燃,导致故障电流很小且随机性强,传统过电流保护装置无法辨识和动作。人工智能技术提高了高阻接地故障检测的灵敏性和准确率。本文首先介绍了高阻接地故障检测数据库的构建方法;然后从信号采集、特征提取以及分类器选取对人工智能在高阻接地故障识别的应用进行了分析和探讨;最后总结了人工智能应用于高阻接地故障检测需要解决的关键问题,为后续相关研究提供了解决思路。
Due to the larger ground resistance,extinguishing and restrike arc,when high impedance faults of distribution network happen,the high impedance fault( HIF) of distribution network has strong randomness and small amplitude fault current,and traditional over-current protection device cannot detect and work. Artificial intelligence( AI) technology improves the sensitivity and accuracy of HIF detection. In this paper,firstly,the construction method of HIF detection database is introduced. Then AI based HIF detection is analyzed and discussed from signal acquisition,feature extraction and classifier selection. Finally,the key problems in the application of AI to HIF detection are summarized,which provide a solution for the subsequent related researches.
Due to the larger ground resistance,extinguishing and restrike arc,when high impedance faults of distribution network happen,the high impedance fault( HIF) of distribution network has strong randomness and small amplitude fault current,and traditional over-current protection device cannot detect and work. Artificial intelligence( AI) technology improves the sensitivity and accuracy of HIF detection. In this paper,firstly,the construction method of HIF detection database is introduced. Then AI based HIF detection is analyzed and discussed from signal acquisition,feature extraction and classifier selection. Finally,the key problems in the application of AI to HIF detection are summarized,which provide a solution for the subsequent related researches.
引文
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[7] GHADERI A,MOHAMH A,GINN H L,et al. High-impedance fault detection in the distribution network using the time-frequency-based algorithm[J]. IEEE Transactions on Power Delivery,2015,30(3):1260-1268.
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[9]李天友,徐丙垠,薛永端.配电网高阻接地故障保护技术及其发展[J].供用电,2018,35(5):2-6.LI Tianyou,XU Bingyin,XUE Yongduan. High impedance fault protection of distribution networks and its development[J]. Distribution&Utilization,2018,35(5):2-6.
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[36] ABDEL MS. An artificial intelligence based approach for high impedance faults analysis in distribution networks under different loading conditions[C]//International Conference on Computer Theory and Applications(ICCTA),October 15-17,2011,Alexandria,Egypt.
[37] SOHEILI A,SADEH J,BAKHSHI R. Modified FFT based high impedance fault detection technique considering distribution non-linear loads:Simulation and experimental data analysis[J].International Journal of Electrical Power&Energy System,2018,94:124-140.
[38] SAMANTARAY S R,DASH P K. High impedance fault detection in distribution feeders using extended Kalman filter and support vector machine[J]. European Transactions on Electrical Power,2010,20(3):382-393.
[39] GOMES D P S,OZANSOY C,ULHAQ A. High-sensitivity vegetation high-impedance fault detection based on signal's highfrequency contents[J]. IEEE Transactions on Power Delivery,2018,33(3):1398-1407.
[40] LIU Y,WU C,WANG Y. Detection of serial arc fault on lowvoltage indoor power lines by using radial basis function neural network[J]. International Journal of Electrical Power&Energy System,2016,83:149-157.
[41] ABDELGAYED T S,MORSI W G,SIDHU T S. A new harmony search approach for optimal w avelets applied to fault classification[J]. IEEE Transactions on Smart Grid,2018,9(2):521-529.
[42] GUO M,ZENG X,CHEN D,et al. Deep-learning-based earth fault detection using continuous w avelet transform and convolutional neural network in resonant grounding distribution systems[J]. IEEE Sensors Journal,2018,18(3):1291-1300.
[43] BAQUI I,ZAMORA I,MAZóN J,et al. High impedance fault detection methodology using w avelet transform and artificial neural networks[J]. Electric Power System Research,2011,81(7):1325-1333.
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[2]朱晓娟,林圣,张姝,等.基于小波能量矩的高阻接地故障检测方法[J].电力自动化设备,2016,36(12):161-168.ZHU Xiaojuan,LIN Sheng,ZHANG Shu,et al. High-impedance grounding fault detection based on w avelet energy moment[J]. Electric Power Automation Equipment,2016,36(12):161-168.
[3] COSTA F B,SOUZA B A,BRITO N S D,et al. Real-time detection of transients induced by high-impedance faults based on the boundary w avelet transform[J]. IEEE Transactions on Industry Application,2015,51(6):5312-5323.
[4] SARWAGYA K,D E S,NAYAK P K. High-impedance fault detection in electrical power distribution systems using moving sum approach[J]. IET Science,Measurement&Technology,2018,12(1):1-8.
[5]王宾,耿建昭,董新洲.配网高阻接地故障伏安特性分析及检测[J].中国电机工程学报,2014,34(22):3815-3823.WANG Bin,GENG Jianzhao,DONG Xinzhou. Analysis and detection of volt-ampere characteristics for high impedance faults in distribution systems[J]. Proceedings of the CSEE,2014,34(22):3815-3823.
[6]耿建昭,王宾.中性点有效接地配电网高阻接地故障特征分析及检测[J].电力系统自动化,2013,37(16):85-91.GENG Jianzhao,WANG Bin. Analysis and detection of high impedance grounding fault in neutral point effectively grounding distribution network[J]. Automation of Electric Power Systems2013,37(16):85-91.
[7] GHADERI A,MOHAMH A,GINN H L,et al. High-impedance fault detection in the distribution network using the time-frequency-based algorithm[J]. IEEE Transactions on Power Delivery,2015,30(3):1260-1268.
[8] MORTAZAVI S H,MORAVEJ Z,SHAHRTASH S M. A hybrid method for arcing faults detection in large distribution networks[J]. International Journal of Electrical Power&Energy System,2018,94:141-150.
[9]李天友,徐丙垠,薛永端.配电网高阻接地故障保护技术及其发展[J].供用电,2018,35(5):2-6.LI Tianyou,XU Bingyin,XUE Yongduan. High impedance fault protection of distribution networks and its development[J]. Distribution&Utilization,2018,35(5):2-6.
[10] TORRES V,GUARDADO J L,RUIZ H F,et al. Modeling and detection of high impedance faults[J]. International Journal of Electrical Power&Energy System,2014(61):163-172.
[11] ZAMANAN N,SYKULSKI J. The evolution of high impedance fault modeling[C]//International Conference on Harmonics and Quality of Power(ICHQP),May 25-28,2014,Bucharest,Romania. New York:IEEE,2014:1-6.
[12] MISHRA M,ROUTRAY P,ROUT P K. A universal high impedance fault detection technique for distribution system using stransform and pattern recognition[J]. Technology and Economics of Smart Grids and Sustainable Energy,2016,1(1):1-14.
[13] GAUTAMS,BRAHMA S M. Detection of high impedance fault in power distribution systems using mathematical morphology[J]. IEEE Transactions on Power System,2013,28(2):1226-1234.
[14]贾旭东,李庚银,赵成勇,等.电力系统仿真可信度评估方法的研究[J].中国电机工程学报,2010,30(19):51-57.JIA Xudong,LI Gengyin,ZHAO Chengyong,et al. Study of the credibility evaluation method for the power system simulation[J]. Proceedings of the CSEE,2010,30(19):51-57.
[15]吴方.时频域分析方法在仿真模型验证中的应用研究[D].哈尔滨:哈尔滨工业大学,2013.
[16] MICHALIK M,LUKOWICZ MA,REBIZANT W,et al. New ANN-based algorithms for detecting Hifs in multi grounded MV networks[J]. IEEE Transactions on Power Delivery,2008,23(1):58-66.
[17] LI K,ZENG X G,YIN X G. Novel methods for high-impedance ground-fault protection in low-voltage supply systems[J].Electric Machines&Power Systems,2003,31(12):1133-1150.
[18] DAS B. Fuzzy logic-based fault-type identification in unbalanced radial power distribution system[J]. IEEE Transactions on Power Delivery,2006,21(1):278-285.
[19] ELKALASHY N I,LEHTONEN M,DARWISH H A,et al. A novel selectivity technique for high impedance arcing fault detection in compensated MV networks[J]. European Transactions on Electrical Power,2008,18(4):344-363.
[20] CHENG J,HUANG S,HSIEH C. Application of Gabor-Wigner transform to inspect high-impedance fault-generated signals[J].International Journal of Electrical Power&Energy System,2015,73:192-199.
[21] LIVANI H,EVRENOSOGLU C Y. A fault classification and localization method for three-terminal circuits using machine learning[J]. IEEE Transactions on Power Delivery,2013,28(4):2282-2290.
[22] BATISTA O E,FLAUZINO R A,DE ARAUJO MA,et al.Methodology for information extraction from oscillograms and its application for high-impedance faults analysis[J]. International Journal of Electrical Power&Energy System,2016,76:23-34.
[23] LAI T M,SNIDER L A,LO E,et al. High-impedance fault detection using discrete w avelet transform and frequency range and RMS conversion[J]. IEEE Transactions on Power Delivery,2005,20(1):397-407.
[24] SARLAK M,SHAHRTASH S M. High impedance fault detection using combination of multi-layer perceptron neural networks based on multi-resolution morphological gradient features of current w aveform[J]. IET Generation,Transmission&Distribution,2011,5(5):588.
[25]郭谋发,游林旭.基于LCD-Hilbert谱奇异值和多级支持向量机的配电网故障识别方法[J].高电压技术,2017,43(4):1239-1247.GUO Moufa,YOU Linxu. Identification method of distribution network faults based on singular value of LCD-Hilbert spectrums and multilevel SVM[J]. High Voltage Engineering,2017,43(4):1239-1247.
[26] SARLAK M,SHAHRTASH S M. High-impedance faulted branch identification using magnetic-field signature analysis[J].IEEE Transactions on Power Delivery,2013,28(1):67-74.
[27] BAHADOR N,NAMDARI F,MATINFAR H R. Tree-related high impedance fault location using phase shift measurement of high frequency magnetic field[J]. International Journal of Electrical Power&Energy System,2018,100:531-539.
[28] MILIOUDIS A N,ANDREOU G T,LABRIDIS D P. Enhanced protection scheme for smart grids using power line communications techniques-part i:detection of high impedance fault occurrence[J]. IEEE Transactions on Smart Grid,2012,3(4):1621-1630.
[29] ZAMORA I,MAZON AJ,SAGASTABEITIA KJ,et al. New method for detecting low current faults in electrical distribution systems[J]. IEEE Transactions on Power Delivery,2007,22(4):2072-2079.
[30] RUZ F,QUIJANO A,GóMEZ E. DSTRP:a new algorithm for high impedance fault detection in compensated neutral grounded MV power systems[J]. International Journal of Electrical Power&Energy System,2003,13(1):23-28.
[31] SOHEILI A,SADEH J. Evidential reasoning based approach to high impedance fault detection in power distribution systems[J]. IET Generation,Transmission&Distribution,2017,11(5):1325-1336.
[32] SEDIGHI A R,HAGHIFAMMR,MALIK O P. Soft computing applications in high impedance fault detection in distribution systems[J]. Electric Power System Research,2005,76(13):136-144.
[33] MAMISHEV V A,RUSSELL B D,BENNER C L. Analysis of high impedance faults using fractal techniques[C]//IEEE Power&Energy Society,1996,11(1):435-440.
[34] SIADATAN A,KAZEMI KAREGAR H,NAJMI V. New high impedance fault detection[C]//2010 IEEE International Conference on Power and Energy,November 29-December 1,2010,Kuala Lumpur,Malaysia. New York:IEEE,2011:573-576.
[35] SAMANTARAY S R,DASH P K,UPADHYAY S K. Adaptive Kalman filter and neural network based high impedance fault detection in power distribution networks[J]. International Journal of Electrical Power&Energy System,2009,31(4):167-172.
[36] ABDEL MS. An artificial intelligence based approach for high impedance faults analysis in distribution networks under different loading conditions[C]//International Conference on Computer Theory and Applications(ICCTA),October 15-17,2011,Alexandria,Egypt.
[37] SOHEILI A,SADEH J,BAKHSHI R. Modified FFT based high impedance fault detection technique considering distribution non-linear loads:Simulation and experimental data analysis[J].International Journal of Electrical Power&Energy System,2018,94:124-140.
[38] SAMANTARAY S R,DASH P K. High impedance fault detection in distribution feeders using extended Kalman filter and support vector machine[J]. European Transactions on Electrical Power,2010,20(3):382-393.
[39] GOMES D P S,OZANSOY C,ULHAQ A. High-sensitivity vegetation high-impedance fault detection based on signal's highfrequency contents[J]. IEEE Transactions on Power Delivery,2018,33(3):1398-1407.
[40] LIU Y,WU C,WANG Y. Detection of serial arc fault on lowvoltage indoor power lines by using radial basis function neural network[J]. International Journal of Electrical Power&Energy System,2016,83:149-157.
[41] ABDELGAYED T S,MORSI W G,SIDHU T S. A new harmony search approach for optimal w avelets applied to fault classification[J]. IEEE Transactions on Smart Grid,2018,9(2):521-529.
[42] GUO M,ZENG X,CHEN D,et al. Deep-learning-based earth fault detection using continuous w avelet transform and convolutional neural network in resonant grounding distribution systems[J]. IEEE Sensors Journal,2018,18(3):1291-1300.
[43] BAQUI I,ZAMORA I,MAZóN J,et al. High impedance fault detection methodology using w avelet transform and artificial neural networks[J]. Electric Power System Research,2011,81(7):1325-1333.
[44] SAMANTARAY S R,PANIGRAHI B K,DASH P K. High impedance fault detection in power distribution networks using timefrequency transform and probabilistic neural network[J]. IET Generation,Transmission&Distribution,2008,2(2):261-270.
[45] LALA H. Continuous wavelet transform and artificial neural network based fault diagnosis in 52 bus hybrid distributed generation system[C]//IEEE Students Conference on Engineering and Systems(SCES),November 6-8,2015,Allahabad,India.New York:IEEE,2016:1-6.
[46] PERERA N,RAJAPAKSE A D. Recognition of fault transients using a probabilistic neural-network classifier[J]. IEEE Transactions on Power Delivery,2011,26(1):410-419.
[47] MANJULA M. Empirical mode decomposition based probabilistic neural network for faults classification[C]//International Conference on Power and Energy Systems,December 22-24,2011,Chennai,India. New York:IEEE,2012:1-5.
[48] MOLOI K. High impedance fault detection technique based on discrete w avelet transform and support vector machine in power distribution networks[C]//2011 IEEE Electronics,Robotics and Automotive Mechanics Conference, November 15-18,2011,Cuernavaca,Mexcio. New York:IEEE,2012:1-6.
[49] SHENG Y,ROVNYAK S M. Decision tree-based methodology for high impedance fault detection[J]. IEEE Transactions on Power Delivery,2004,19(2):533-536.
[50] SAMANTARAY S R. Ensemble decision trees for high impedance fault detection in power distribution network[J]. International Journal of Electrical Power&Energy System,2012,43(1):1048-1055.
[51] CHEN W,LIU C,Tsai M. On-line fault diagnosis of distribution substations using hybrid cause-effect network and fuzzy rulebased method[J]. IEEE Transactions Power Deliver,2000,15(2):710-717.
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