基于行波特征分类的有源配电网故障定位
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TM755

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国家自然科学基金资助项目(61201407,71971029);陕西省自然科学基础研究计划(2016JQ5103,2019GY-002);长安大学中央高校基本科研业务费专项资金资助项目(300102321504,300102321501,300102321503)。


Fault location of active distribution network based on traveling wave feature classification
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    摘要:

    随着分布式电源(distributed generator,DG)的接入,配电网的潮流方向和结构发生改变,许多传统配电网的故障定位方法已不再适用。单相接地故障是配电网常见故障且可能带来二次故障乃至断电等危害,从线模行波小波特征值与含DG的配电线路故障区段之间的关系入手,通过线性判别分析(linear discriminant analysis,LDA)降维挑选出最优故障特征,再利用机器学习中与该模型契合最好的基于核分布的贝叶斯构造分类模型,实现单相接地故障定位新方法。构建含DG的IEEE 33节点模型对有源配电网不同区段的故障进行实验,得出最优三维特征样本的定位准确率为97.9%,结果表明该方法能实现故障的准确定位。

    Abstract:

    With the access of distributed generator (DG), the power flow direction and structure of distribution networks have changed. Therefore, many traditional fault location methods are no longer applicable. Single-phase-to-ground fault is a common fault in distribution networks, which may bring secondary fault and even blackout. Based on the relationship between the wavelet eigenvalues of traveling waves and the fault section of distribution lines with DG, the optimal fault features were selected by dimension reduction of linear discriminant analysis (LDA), and then the Bayesian construction classification model based on kernel distribution was used to realize a new method of single-phase-to-ground fault location. The IEEE 33 bus model with DG was constructed to test the faults in different sections of active distribution networks. The location accuracy of the optimal three-dimensional feature sample reached 97.9%, demonstrating that the proposed method can achieve accurate fault location.

    参考文献
    [1] 薛永端, 李娟, 徐丙垠. 中性点经消弧线圈接地系统小电流接地故障暂态等值电路及暂态分析[J]. 中国电机工程学报, 2015, 35(22):5703-5714.Xue Y R, Li J, Xu B Y. Transient equivalent circuit and transient analysis of single-phase earth fault in arc suppression coil grounded system[J]. Proceedings of the CSEE, 2015, 35(22):5703-5714. (in Chinese)
    [2] Wang X W, Zhang H X, Shi F, et al. Location of single phase to ground faults in distribution networks based on synchronous transients energy analysis[J]. IEEE Transactions on Smart Grid, 2020, 11(1):774-785.
    [3] 陈奎, 张云, 王洪寅, 等. 基于免疫算法的含分布式电源配电网的故障定位[J]. 电力系统保护与控制, 2017, 45(24):57-62.Chen K, Zhang Y, Wang H Y, et al. Fault-section location of distribution network containing distributed generation based on immune algorithm[J]. Power System Protection and Control, 2017, 45(24):57-62. (in Chinese)
    [4] 廖志伟, 叶青华, 王钢, 等. 基于GRNN的多故障自适应电力系统故障诊断[J]. 华南理工大学学报(自然科学版), 2005(9):6-9.Liao Z W, Ye Q H, Wang G, et al. Adaptive multi-fault diagnosis of power system based on GRNN[J]. Journal of South China University of Technology(Natural Science Edition), 2005(9):6-9. (in Chinese)
    [5] Guo M F, Gao J H, Shao X, et al. Location of single-line-to-ground fault using 1-D convolutional neural network and waveform concatenation in resonant grounding distribution systems[J]. IEEE Transactions on Instrumentation and Measurement, 2021(70):1-9.
    [6] Xie L W, Luo L F, Li Y, et al. A traveling wave-based fault location method employing vmd-teo for distribution network[J]. IEEE Transactions on Power Delivery, 2020, 35(4):1987-1998.
    [7] Shi S X, Zhu B E, Lei A Y et al. Fault location for radial distribution network VIA topology and reclosure-generating traveling waves[J]. IEEE Transactions on Smart Grid, 2019,10(6):6404-6413.
    [8] Bahmanyar A, Jamali S. Fault location in active distribution networks using non-synchronized measurements[J]. International Journal of Electrical Power & Energy Systems, 2017(93):451-458.
    [9] Zhang T, Li X H, Yu H B, et al. A fault location method for active distribution network with renewable sources based on bp neural network[C]//2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics. Hangzhou, China, 2015:357-361.
    [10] 梁睿, 崔连华, 都志立, 等. 基于广域行波初始波头时差关系矩阵的配电网故障选线及测距[J]. 高电压技术, 2014, 40(11):3411-3417.Liang R, Cui L H, Du Z L, et al. Fault line selection and location in distribution power network based on traveling wave time difference of arrival relationships[J]. High Voltage Engineering, 2014, 40(11):3411-3417. (in Chinese)
    [11] 刘科研, 董伟杰, 肖仕武, 等.基于电压数据SVM分类的有源配电网故障判别及定位[J]. 电网技术, 2021, 45(6):2369-2379.Liu K Y, Dong W J, Xiao S W, et al. Fault identification and location of active distribution network based on SVM classification of voltage data. power system technology[J]. Power System Technology, 2021, 45(6):2369-2379. (in Chinese)
    [12] Pourahmadi-Nakhli M, Safavi A A. Path characteristic frequency-based fault locating in radial distribution systems using wavelets and neural networks[J]. IEEE Transactions on Power Delivery, 2011, 26(2):772-781.
    [13] Qiao J, Yin X G, Liu X Y, et al. An accurate fault-location method for distribution network with distributed generators based on multi-terminal traveling wave[C]//2020 IEEE Sustainable Power and Energy Conference (iSPEC), 2020:1867-1873.
    [14] 贾科, 董雄鹰, 李论, 等. 基于稀疏电压幅值量测的配电网故障测距[J]. 电网技术, 2020, 44(3):835-845.Jia K, Dong X Y, Li L, et al. Fault location for distribution network based on transient sparse voltage amplitude measurement[J]. Power System Technology, 2020, 44(3):835-845. (in Chinese)
    [15] 梁睿, 靳征, 王崇林, 等. 行波时频复合分析的配电网故障定位研究[J].中国电机工程学报, 2013, 33(28):130-136.Liang R, Jin Z, Wang C L, et al. Research of fault location in distribution networks based on integration of travelling wave time and frequency analysis[J]. Proceedings of the CSEE, 2013, 33(28):130-136. (in Chinese)
    [16] Akbar M A, Ali A A S, Amira A, et al. An empirical study for PCA- and LDA-based feature reduction for gas identification[J]. IEEE Sensors Journal, 2016, 16(14):5734-5746.
    [17] Nguyen Q H, Ly H B, Ho L S, et al. Influence of data splitting on performance of machine learning models in prediction of shear strength of soil[J]. Mathematical Problems in Engineering, 2021, 2021:4832864.
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徐先峰,徐晨杰,张艳波,赵依,王世鑫.基于行波特征分类的有源配电网故障定位[J].重庆大学学报,2022,45(11):59-68.

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  • 收稿日期:2021-03-22
  • 在线发布日期: 2022-12-01
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