基于Monte Carlo方法的中性点非有效接地系统输电线路故障测距
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摘要
中性点非有效接地系统被广泛应用于我国中低压配电网络,在此系统中单相接地故障是主要的故障类型。发生故障时在接地点故障电流不太大的情况下,电力系统可以带故障运行一段时间,系统有较高可靠性。但这种接地方式也存在缺点:故障电阻的存在、线路参数(特别是零序参数)不准确、测量装置存在误差等再加上本身故障电流很小,使测距的精确度降低。这样就降低了供电可靠性,故找出一种新思路使其能更准确有效地判断故障点具有重要意义。
接地故障中,零序阻抗和大地电导率存在直接联系,其值往往随着土壤的湿度、盐度等变化而变化,并非为一定值,故很难测得故障发生时刻的精确值。本文在搜集统计中国各个地区多年土壤湿度的基础上,推导计算并分析了零序阻抗在不同地区,不同时间段的分布。
Monte Carlo方法是以概率统计理论为指导的一类非常重要的数值计算方法,其通过使用随机数(或更常见的伪随机数)来解决很多计算问题。本文提出了应用Monte Carlo进行故障测距的新方法。该算法利用零序阻抗的统计信息及其分布规律,求取故障距离的某些数学特征,使在零序阻抗在有大范围变化的情况下得到的测距精度更高。本文提出的算法基于故障线路的建模及Monte- Carlo方法。这种算法不仅能算出故障点,也能求出另一个重要信息,即短路最可能发生的线路段。
Non-direct grounded neutral system has been widely used in low and medium voltage distribution system in some countries and the single phrase to ground fault is the main one of this kind of system. When the fault occurred, if the value of the fault current is not too great, the power system can keep operation for a while with the fault. Therefore, this kind of system brings strong power supply reliability. However this system also has a huge disadvantage that the accuracy of fault location is low because of the fault resistance, inaccurate line parameters (especially the zero-sequence impedance), errors of measuring device and the low fault current by its own, which reduces the power supply reliability. So it is necessary to find some ways for the fault location detection.
In the phase-to-earth fault power system, the value of zero-sequence impedance is directly related to the earth conductivity. When the soil moisture and salinity change, the value of this impedance is different too. So it is hard to determine the exact value when the fault occurred. The value and distribution of zero-sequence impedance in different areas and periods both are calculated in this paper based on the full-scale soil moisture data.
Monte Carlo is an important method that is based on the theory of probability and statistics, which utilizes random number (pseudo-random number is common) to solve many calculative questions. The paper presents a new approach for calculation of fault location. The proposed algorithm utilizes statistical information about the zero-sequence impedances and the distribution laws of these values to get the numerical characteristics of fault location, which results in more accurate fault location for lines with a great interval of the zero-sequence impedance. This algorithm is based on modeling of the faulted line and the method of Monte Carlo. It calculates not only the expected value of the distance to the fault location, namely, the length of the line segment, where short circuit may have occurred.
接地故障中,零序阻抗和大地电导率存在直接联系,其值往往随着土壤的湿度、盐度等变化而变化,并非为一定值,故很难测得故障发生时刻的精确值。本文在搜集统计中国各个地区多年土壤湿度的基础上,推导计算并分析了零序阻抗在不同地区,不同时间段的分布。
Monte Carlo方法是以概率统计理论为指导的一类非常重要的数值计算方法,其通过使用随机数(或更常见的伪随机数)来解决很多计算问题。本文提出了应用Monte Carlo进行故障测距的新方法。该算法利用零序阻抗的统计信息及其分布规律,求取故障距离的某些数学特征,使在零序阻抗在有大范围变化的情况下得到的测距精度更高。本文提出的算法基于故障线路的建模及Monte- Carlo方法。这种算法不仅能算出故障点,也能求出另一个重要信息,即短路最可能发生的线路段。
Non-direct grounded neutral system has been widely used in low and medium voltage distribution system in some countries and the single phrase to ground fault is the main one of this kind of system. When the fault occurred, if the value of the fault current is not too great, the power system can keep operation for a while with the fault. Therefore, this kind of system brings strong power supply reliability. However this system also has a huge disadvantage that the accuracy of fault location is low because of the fault resistance, inaccurate line parameters (especially the zero-sequence impedance), errors of measuring device and the low fault current by its own, which reduces the power supply reliability. So it is necessary to find some ways for the fault location detection.
In the phase-to-earth fault power system, the value of zero-sequence impedance is directly related to the earth conductivity. When the soil moisture and salinity change, the value of this impedance is different too. So it is hard to determine the exact value when the fault occurred. The value and distribution of zero-sequence impedance in different areas and periods both are calculated in this paper based on the full-scale soil moisture data.
Monte Carlo is an important method that is based on the theory of probability and statistics, which utilizes random number (pseudo-random number is common) to solve many calculative questions. The paper presents a new approach for calculation of fault location. The proposed algorithm utilizes statistical information about the zero-sequence impedances and the distribution laws of these values to get the numerical characteristics of fault location, which results in more accurate fault location for lines with a great interval of the zero-sequence impedance. This algorithm is based on modeling of the faulted line and the method of Monte Carlo. It calculates not only the expected value of the distance to the fault location, namely, the length of the line segment, where short circuit may have occurred.
引文
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[5] Qing-chao Zhang, Yao Zhang, and Wen-nan Song, Transmission line fault location for single-phase-to-earth fault on non-direct-ground neutral system[J], IEEE Transactions on Power Deliver, 1998, 13(4):1086~1090
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[8]范春菊,张兆宁,郁惟镛,小波方法在超高压输电线行波故障测距中的应用[J].电网技术,2003,27(8):50～53
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[12] M. Kezunovic, B. Perunicic, Synchronized Sampling Improves Fault Location[J], IEEE Computer Applications in Power, 1995, 30~33
[13] Wu Ping, Zhang Yao, Location Using One-Terminal Data For Transmission Line[J], Proceedings of The Csu-epsa, 2003, 15(4):5~7
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[24]束洪春,许承斌,徐贤敏,10KV或6KV直配线路单相接地故障测距的新算法[J],中国电机工程学报,1995,15(5):423～428
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[28] Francesco Mattia, Giuseppe Satalino, et al. Using a Priori Information to Improve Soil Moisture Retrieval From ENVISAT ASAR AP Data in Semiarid Regions[J] , IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(4):900~912
[29] P. C. Dubois, J. van Zyl, and T. Engman, Measuring soil moisture with imaging radars[J], IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(4):915~926
[30] W. Wagner and K. Scipal, Large-scale soil moisture mapping in western Africa using the ERS scatterometer[J], IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(4):1777~1782
[31]白慧东,刘焱选,初振东等,滨海平原区土壤电导率的空间变异规律研究[J],石河子大学学报(自然科学版),2007,25(5):1～4
[32]张文君,周天军,宇如聪,中国土壤湿度的分布与变化I.多种资料间的比较[J],大气科学,2008,32(3):581～597
[33]张文君,宇如聪,周天军,中国土壤湿度的分布与变化II.耦合模式模拟结果评估,大气科学,2008,32(5):1128～1146
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[40]刘广明,杨劲松,土壤含盐量与土壤电导率及水分含量关系的试验研究,土壤通报,2001,32:85～86
[41] A. Sankarakrishnan, R. Billinton, Sequential Monte Carlo Simulation for Composite Power System Reliability Analysis with Time Varying Loads[J], IEEE Transactions on Power Systems, 1995, 10(3):1540~1545
[42] A. A. Chowdhury, L. Bertling, et al. A Monte Carlo Simulation Model for Multi-Area Generation Reliability Evaluation[C], 9th International Conference on Probabilistic Methods Applied to Power Systems KTH, Stockholm, Sweden-June 11-15, 2006
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[44] Antans Sauhats, Aleksandrs Jonins, et al. Fault Location Algorithms for Power Transmission Lines Based on Monte-Carlo Method[C], IEEE Porto Tech Conference 10th -13th , Sep.2001
[45]刘健,杨文宇,赵高长,基于蒙特卡罗分析的配电网架规划方法比较[J],中国电机工程学报,2006,26(10):73～78
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[52]周概容,概率论与数理统计[M],北京:高等教育出版社,1984,238-240
[2]陈虬,刘先斌,随机有限元法及其工程应用[M],成都:西南交通大学出版社,1993,60～72
[3]韩祯祥,吴国炎,电力系统分析[M],杭州:浙江大学出版社,2005,261～262
[4]王振华,中性点非有效接地系统单相接地故障选线的研究[硕士学位论文],天津:天津大学,2007,9～10
[5] Qing-chao Zhang, Yao Zhang, and Wen-nan Song, Transmission line fault location for single-phase-to-earth fault on non-direct-ground neutral system[J], IEEE Transactions on Power Deliver, 1998, 13(4):1086~1090
[6]张兆宁,毛鹏,郁惟镛,时间序列小波神经网络在故障测距中的应用[J],中国电机工程学报,2001,21(6):66～71
[7]覃剑,输电线路单端行波故障测距的研究[J],电网技术,2005,29(15):65～70
[8]范春菊,张兆宁,郁惟镛,小波方法在超高压输电线行波故障测距中的应用[J].电网技术,2003,27(8):50～53
[9]桑在中,潘贞存,李磊,“S注入法”与选线测距定位[J],电力系统及其自动化学报,1998,10(4):35～39
[10]张惠芬,潘贞存,桑在中,基于注入法的小电流接地系统故障定位新方法[J],电力系统及其自动化,2004,28(3):64～66
[11] M. Kezunovic, J. Mrkic, and B. Perunicic, An Accurate Fault Location Algorithm Using Synchronized Sampling[J], Electric Power System Research, 1994, 29(3)
[12] M. Kezunovic, B. Perunicic, Synchronized Sampling Improves Fault Location[J], IEEE Computer Applications in Power, 1995, 30~33
[13] Wu Ping, Zhang Yao, Location Using One-Terminal Data For Transmission Line[J], Proceedings of The Csu-epsa, 2003, 15(4):5~7
[14]葛耀中,新型继电保护和故障测距的原理与技术(第2版)[M],西安:西安交通大学出版社,2007,256~333
[15]吴必信,陈冬菊,综述单端故障测距算法(一)[J],电力自动化设备,1995,(3):30～34
[16]吴必信,王玉秋,综述单端故障测距算法(二)[J],电力自动化设备,1995,(4):23～25
[17]吴必信,综述单端故障测距算法(三)[J],电力自动化设备,1996,(1):15-19
[18]陈玥云,覃剑,王欣等,配电网故障测距综述[J],电网技术,2006,30(18):89～92
[19]侯自立,彭兰芳,树枝型电力网短路故障的端口比值分支定位法[J],电子科学学刊,1990,12(6):641～644
[20]伊贵业,杨学昌,吴振升,配电网接地故障定位的传递函数法[J],清华大学学报(自然科学版),2000,40(7):31～34
[21]吴振升,杨学昌,配电网接地故障定位传递函数法的试验[J],电力系统自动化,2003,27(11):34～37
[22]吴振升,杨学昌,伊贵业,基于频域分析法的配电网分支故障定位[J],电力系统自动化,2001,(23):32～36
[23]李孟秋,王耀南,王辉等,小电流接地系统单相接地故障点探测方法的研究[J],中国电机工程学报,2001,21(10):6～9
[24]束洪春,许承斌,徐贤敏,10KV或6KV直配线路单相接地故障测距的新算法[J],中国电机工程学报,1995,15(5):423～428
[25]贺家李,宋从矩,电力系统继电保护原理(第三版)[M],北京:中国电力出版社,2004.56~60
[26]要焕年,曹梅月,电力系统谐振接地[M],北京:中国电力出版社,2000
[27]陈亚,任建文,不同接地方式配电系统的单相接地故障仿真分析[J],继电器,2005,33(5):67～71
[28] Francesco Mattia, Giuseppe Satalino, et al. Using a Priori Information to Improve Soil Moisture Retrieval From ENVISAT ASAR AP Data in Semiarid Regions[J] , IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(4):900~912
[29] P. C. Dubois, J. van Zyl, and T. Engman, Measuring soil moisture with imaging radars[J], IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(4):915~926
[30] W. Wagner and K. Scipal, Large-scale soil moisture mapping in western Africa using the ERS scatterometer[J], IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(4):1777~1782
[31]白慧东,刘焱选,初振东等,滨海平原区土壤电导率的空间变异规律研究[J],石河子大学学报(自然科学版),2007,25(5):1～4
[32]张文君,周天军,宇如聪,中国土壤湿度的分布与变化I.多种资料间的比较[J],大气科学,2008,32(3):581～597
[33]张文君,宇如聪,周天军,中国土壤湿度的分布与变化II.耦合模式模拟结果评估,大气科学,2008,32(5):1128～1146
[34] Douville H, Viterbo P, Mahfouf j f, et al. Evaluation of the optimal interpolation and nudging techniques for soil moisture analysis using FIFE data[J], Mon. Wea. Rev., 2000, 128:1733～1756
[35] Mahfouf J F, Viterbo P, Douville H, et al. A revised land surface analysis scheme in the integrated forecasting system[J], ECMWF Newsletter, 2000, 88:8～13
[36]周涛,史培军,范一大,中国北方土壤湿度变化趋势及人类活动对其影响的研究[J],北京师范大学学报(自然科学版),2002,38(1):131～137
[37]张祯,荀久玉,孔锦,土壤电导率的测定中影响因素研究[J],工程技术
[38]孙宇瑞,土壤含水率和盐分对土壤电导率的影响[J],中国农业大学学报,2000,5(4):39～41
[39]孙宇瑞,汪懋华,一种土壤电导率测量方法的数学建模与实验研究[J],农业工程,2001,17(2):20～23
[40]刘广明,杨劲松,土壤含盐量与土壤电导率及水分含量关系的试验研究,土壤通报,2001,32:85～86
[41] A. Sankarakrishnan, R. Billinton, Sequential Monte Carlo Simulation for Composite Power System Reliability Analysis with Time Varying Loads[J], IEEE Transactions on Power Systems, 1995, 10(3):1540~1545
[42] A. A. Chowdhury, L. Bertling, et al. A Monte Carlo Simulation Model for Multi-Area Generation Reliability Evaluation[C], 9th International Conference on Probabilistic Methods Applied to Power Systems KTH, Stockholm, Sweden-June 11-15, 2006
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