电气工程学报 ›› 2023, Vol. 18 ›› Issue (3): 297-306.doi: 10.11985/2023.03.032
李华1(), 朱一民1(
), 马海军1, 丁继波1, 楚恬歆2, 张文海2
收稿日期:
2022-09-12
修回日期:
2023-03-24
出版日期:
2023-09-25
发布日期:
2023-10-23
作者简介:
李华,男,1968年生,硕士,高级工程师。主要研究方向为配电设备运维检修与管理。E-mail:15010082@chnenergy.com.cn
LI Hua1(), ZHU Yimin1(
), MA Haijun1, DING Jibo1, CHU Tianxin2, ZHANG Wenhai2
Received:
2022-09-12
Revised:
2023-03-24
Online:
2023-09-25
Published:
2023-10-23
摘要:
早期故障为永久性故障的先兆,及时准确识别系统中早期故障对于实现故障预警、减少永久性故障发生有重要意义。针对小电流接地系统早期故障特征弱、检测识别难度大的难题,提出一种基于最小二乘支持向量机的早期故障识别方法。首先分别基于物理特性和统计特性提取扰动浅层特征集,并基于S变换获取不同频段的能量熵和奇异熵;随后采用最大相关最小冗余法,在保留强相关特征量的同时降低特征集数据维度,构建最优扰动特征集;最后利用PSCAD/EMTDC仿真系统获取各类型扰动样本集,基于最小二乘支持向量机获取早期故障扰动波形识别模型,并采用粒子群算法对支持向量机参数寻优,提高算法效率。根据大量仿真算例分析,所提算法能准确识别小电流接地系统早期故障,验证了其正确性和有效性。
中图分类号:
李华, 朱一民, 马海军, 丁继波, 楚恬歆, 张文海. 基于最小二乘支持向量机的小电流接地系统早期故障识别算法研究[J]. 电气工程学报, 2023, 18(3): 297-306.
LI Hua, ZHU Yimin, MA Haijun, DING Jibo, CHU Tianxin, ZHANG Wenhai. Least Squares Support Vector Machine Based Incipient Fault Identification in Non-solidly Grounding System[J]. Journal of Electrical Engineering, 2023, 18(3): 297-306.
[1] | 李宇, 杨柳林. 基于卷积神经网络的配电网单相接地故障识别[J]. 电气工程学报, 2020, 15(3):22-30. |
LI Yu, YANG Liulin. Identification of single phase to earth fault in distribution network based on convolution neural network[J]. Journal of Electrical Engineering, 2020, 15(3):22-30. | |
[2] |
STRINGER N T, KOJOVIC L A. Prevention of underground cable splice failures[J]. IEEE Transactions on Industry Applications, 2001, 37(1):230-239.
doi: 10.1109/28.903154 |
[3] |
KULKARNI S, SANTOSO S, SHORT T A. Incipient fault location algorithm for underground cables[J]. IEEE Transactions on Smart Grid, 2014, 5(3):1165-1174.
doi: 10.1109/TSG.2014.2303483 |
[4] |
SHI S, HU Z, MA Z, et al. Travelling waves-based identification of sub-health condition of feeders in power distribution system[J]. IET Generation Transmission & Distribution, 2018, 12(5):1067-1073.
doi: 10.1049/gtd2.v12.5 |
[5] |
JIANG J, CHEN R, CHEN M, et al. Dynamic fault prediction of power transformers based on hidden Markov model of dissolved Gases analysis[J]. IEEE Transactions on Power Delivery, 2019, 34(4):1393-1400.
doi: 10.1109/TPWRD.61 |
[6] | 刘亚东, 丛子涵, 严英杰, 等. 配电设备早期故障检测基本原理、关键技术与发展趋势[J]. 供用电, 2020, 37(4):10-16,32. |
LIU Yadong, CONG Zihan, YAN Yingjie, et al. Basic principles,key technologies and development trends of incipient fault detection for power distribution equipments[J]. Distribution & Utilization, 2020, 37(4):10-16,32. | |
[7] | 戴铭. 10 kV地下电缆早期故障检测与识别方法探讨[D]. 成都: 西南交通大学, 2015. |
DAI Ming. Discussion methods of incipient fault detection and identification in 10 kV underground cables[D]. Chengdu: Southwest Jiaotong University, 2015. | |
[8] | KASZTENY B, VOLOH I, JONES C G, et al. Detection of incipient faults in underground medium voltage cables[C]// IEEE Protective Relay Engineers 61st Annual Conference, April 1-3,2008,Texas A&M University,College Station,Texas. Piscataway:IEEE, 2008:349-366. |
[9] |
SAMET H, TAJDINIAN M, KHALEGHIAN S, et al. A statistical-based criterion for incipient fault detection in underground power cables established on voltage waveform characteristics[J]. Electric Power Systems Research, 2021, 197:107303.
doi: 10.1016/j.epsr.2021.107303 |
[10] |
GHANBARI T. Kalman filter based incipient fault detection method for underground cables[J]. IET Generation,Transmission & Distribution, 2015, 9(14):1988-1997.
doi: 10.1049/gtd2.v9.14 |
[11] |
ZHANG W, XIAO X, ZHOU K, et al. Multi-cycle incipient fault detection and location for medium voltage underground cable[J]. IEEE Transactions on Power Delivery, 2017, 32(3):1450-1459.
doi: 10.1109/TPWRD.2016.2615886 |
[12] | 熊思衡, 刘亚东, 方健, 等. 配电线路早期故障辨识方法[J]. 高电压技术, 2020, 46(22):3970-3976. |
XIONG Siheng, LIU Yadong, FANG Jian, et al. Detection method of incipient faults of power system lines[J]. High Voltage Engineering, 2020, 46(22):3970-3976. | |
[13] | 汪颖, 卢宏, 杨晓梅, 等. 堆叠自动编码器与S变换相结合的电缆早期故障识别方法[J]. 电力自动化设备, 2018, 38(8):117-124. |
WANG Ying, LU Hong, YANG Xiaomei, et al. Cable incipient fault identification based on stacked autoencoder and S-transform[J]. Electric Power Automation Equipment, 2018, 38(8):117-124. | |
[14] | 汪颖, 孙建风, 肖先勇, 等. 基于优化卷积神经网络的电缆早期故障分类识别[J]. 电力系统保护与控制, 2020, 48(7):10-18. |
WANG Ying, SUN Jianfeng, XIAO Xianyong, et al. Cable incipient fault classification and identification based on optimized convolution neural network[J]. Power System Protection and Control, 2020, 48(7):10-18. | |
[15] |
WENG Y, CUI Q, GUO M. Transform waveforms into signature vectors for general-purpose incipient fault detection[J]. IEEE Transactions on Power Delivery, 2022, 37(6):4559-4569.
doi: 10.1109/TPWRD.2022.3151110 |
[16] | 刘健, 张小庆, 张志华, 等. 提升小电流接地系统单相接地故障处理能力[J]. 供用电, 2021, 38(10):52-56. |
LIU Jian, ZHANG Xiaoqing, ZHANG Zhihua, et al. Solutions to improve the single-phase grounding fault management of neural non-effective grounded systems[J]. Distribution & Utilization, 2021, 38(10):52-56. | |
[17] | 楚恬歆, 张文海, 瞿科, 等. 小电流接地系统接地型早期故障扰动特征分析[J]. 电力系统保护与控制, 2021, 49(18):52-61. |
CHU Tianxin, ZHANG Wenhai, QU Ke, et al. Grounded incipient fault analysis in a non-solidly grounding system[J]. Power System Protection and Control, 2021, 49(18):52-61. | |
[18] | 楚恬歆, 张文海, 瞿科, 等. 基于复合判据的小电流接地系统接地型早期故障检测[J]. 高电压技术, 2022, 48(3):1022-1031. |
CHU Tianxin, ZHANG Wenhai, QU Ke, et al. The grounded incipient fault detection based on compound criterion in non-solidly grounding system[J]. High Voltage Engineering, 2022, 48(3):1022-1031. | |
[19] | 陶维青, 夏熠, 陆鼎堃. S变换熵理论及其在电力系统故障检测中的应用研究[J]. 合肥工业大学学报, 2016, 39(1):40-45. |
TAO Weiqing, XIA Yi, LU Dingkun. Study of S-transform entropy theory and its application in fault detection of electric power system[J]. Journal of Hefei University of Technology, 2016, 39(1):40-45. | |
[20] |
程玉胜, 宋帆, 王一宾, 等. 基于专家特征的条件互信息多标记特征选择算法[J]. 计算机应用, 2020, 40(2):503-509.
doi: 10.11772/j.issn.1001-9081.2019091626 |
CHENG Yusheng, SONG Fan, WANG Yibin, et al. Multi-label feature selection algorithm based on conditional mutual information of expert feature[J]. Journal of Computer Applications, 2020, 40(2):503-509.
doi: 10.11772/j.issn.1001-9081.2019091626 |
|
[21] |
毛莺池, 曹海, 平萍, 等. 基于最大联合条件互信息的特征选择[J]. 计算机应用, 2019, 39(3):734-741.
doi: 10.11772/j.issn.1001-9081.2018081694 |
MAO Yingchi, CAO Hai, PING Ping, et al. Feature selection based on maximum conditional and joint mutual information[J]. Journal of Computer Application, 2019, 39(3):734-741. | |
[22] | 盖晓平, 王冬青, 赵喜兰, 等. 利用概率统计特性的保护告警信息特征降维方法[J]. 电网技术, 2021, 45(5):2017-2024. |
GAI Xiaoping, WANG Dongqing, ZHAO Xilan, et al. Feature reduction method for alarm information protection with probability statistical characteristics[J]. Power System Technology, 2021, 45(5):2017-2024. | |
[23] | 李扬, 顾雪平. 基于改进最大相关最小冗余判据的暂态稳定评估特征选择[J]. 中国电机工程学报, 2013, 33(34):179-186. |
LI Yang, GU Xueping. Feature selection for transient stability assessment based on improved maximal relevance and minimal redundancy criterion[J]. Proceedings of the CSEE, 2013, 33(34):179-186. | |
[24] | 孙志鹏, 孙志龙, 魏建. 基于决策树支持向量机算法的电力变压器故障诊断研究[J]. 电气工程学报, 2019, 14(4):42-45. |
SUN Zhipeng, SUN Zhilong, WEI Jian. Research on power transformer fault diagnosis based on decision tree support vector machine[J]. Journal of Electrical Engineering, 2019, 14(4):42-45. | |
[25] | 徐世晖. 基于改进在线最小二乘支持向量机电池故障诊断方法研究[D]. 哈尔滨: 哈尔滨理工大学, 2020. |
XU Shihui. Research on battery fault diagnosis method based on improved online least square support vector machine[D]. Harbin:Harbin University of Science and Technology, 2021. | |
[26] | 邓乃扬, 田英杰. 数据挖掘中的新方法——支持向量机[M]. 北京: 科学出版社, 2004. |
DENG Naiyang, TIAN Yingjie. A new method of data mining:Support vector machines[M]. Beijing: China Science Publishing & Media Ltd., 2004. | |
[27] | 郑含博, 王伟, 李晓纲, 等. 基于多分类最小二乘支持向量机和改进粒子群优化算法的电力变压器故障诊断方法[J]. 高电压技术, 2014, 40(11):3424-3429. |
ZHENG Hanbo, WANG Wei, LI Xiaogang, et al. Fault diagnosis method of power transformers using multi-class LS-SVM and improved PSO[J]. High Voltage Engineering, 2014, 40(11):3424-3429. | |
[28] |
LIN S, YING K, CHEN S, et al. Particle swarm optimization for parameter determination and feature selection of support vector machines[J]. Expert Systems with Applications, 2008, 35(4):1817-1824.
doi: 10.1016/j.eswa.2007.08.088 |
[29] | 冯茜, 李擎, 全威, 等. 多目标粒子群优化算法研究综述[J]. 工程科学学报, 2021, 43(6):745-753. |
FENG Qian, LI Qing, QUAN Wei, et al. Overview of multiobjective particle swarm optimization algorithm[J]. Chinese Journal of Engineering, 2021, 43(6):745-753. | |
[30] | 王文凯, 邓斌. 基于DAE-IPSO-SVM的电缆早期故障识别方法[J]. 国外电子测量技术, 2021, 40(8):29-35. |
WANG Wenkai, DENG Bin. Cable incipient fault identification method based on DAE-IPSO-SVM[J]. Foreign Electronic Measurement Technology, 2021, 40(8):29-35. | |
[31] |
CHANG G, HONG Y, LI G. A hybrid intelligent approach for classification of incipient faults in transmission network[J]. IEEE Transactions on Power Delivery, 2019, 34(4):1785-1794.
doi: 10.1109/TPWRD.61 |
[32] |
杨杏丽. 分类学习算法的性能度量指标综述[J]. 计算机科学, 2021, 48(8):209-219.
doi: 10.11896/jsjkx.200900216 |
YANG Xingli. Survey of performance measure index of classification learning algorithm[J]. Computer Science, 2021, 48(8):209-219.
doi: 10.11896/jsjkx.200900216 |
[1] | 胡文扬, 王天杨, 张飞斌, 褚福磊. 仿真驱动下基于Ramanujan周期变换的轴承早期故障特征提取[J]. 机械工程学报, 2023, 59(13): 148-156. |
[2] | 徐彬翔, 郑林锋, 黄乙恒, 肖志能, 王新月. 基于改进最小二乘支持向量机的锂离子电池健康状态快速估计方法*[J]. 电气工程学报, 2022, 17(4): 11-19. |
[3] | 孙誉宁, 毛磊, 黄伟国, 章恒, 陆守香. 基于磁场的质子交换膜燃料电池故障诊断方法[J]. 机械工程学报, 2022, 58(22): 106-114. |
[4] | 宋立业, 范抑伶, 王燚增. 基于KPCA与IHHO-LSSVM的电力变压器故障诊断方法研究*[J]. 电气工程学报, 2022, 17(1): 95-103. |
[5] | 谯自健, 束学道. 非对称势诱导随机共振增强机械重复瞬态提取[J]. 机械工程学报, 2021, 57(23): 160-168. |
[6] | 董绍江, 裴雪武, 汤宝平, 田科位, 朱朋, 李洋, 赵兴新. 基于FNER性能退化指标及IDRSN的滚动轴承寿命状态识别方法[J]. 机械工程学报, 2021, 57(15): 105-115. |
[7] | 舒星, 刘永刚, 申江卫, 陈峥. 基于改进最小二乘支持向量机与Box-Cox变换的锂离子电池容量预测[J]. 机械工程学报, 2021, 57(14): 118-128. |
[8] | 王庆锋, 卫炳坤, 刘家赫, 马文生, 许述剑. 一种数据驱动的旋转机械早期故障检测模型构建和应用研究[J]. 机械工程学报, 2020, 56(16): 22-32. |
[9] | 李锋, 汤宝平, 王家序, 林建辉. 基于图嵌入概率半监督判别分析的故障辨识*[J]. 机械工程学报, 2017, 53(9): 92-100. |
[10] | 孙鲜明, 刘欢, 赵新光, 周勃. 基于瞬时包络尺度谱熵的滚动轴承早期故障奇异点识别及特征提取*[J]. 机械工程学报, 2017, 53(3): 73-80. |
[11] | 何孟凡,艾红. 基于RS和LS-SVM的回转窑主传动电流预测研究[J]. 电气工程学报, 2017, 12(11): 21-27. |
[12] | 张云强, 张培林, 王怀光, 吴定海. 基于双时域微弱故障特征增强的轴承早期故障智能识别*[J]. 机械工程学报, 2016, 52(21): 96-103. |
[13] | 王磊,骆玮,曹现峰. 基于改进EMD算法的小电流接地故障选线[J]. 电气工程学报, 2015, 10(8): 29-34. |
[14] | 李宏坤;刘洪轶;徐福健;张晓雯;张学峰. 连续小波最优重构尺度确定方法与故障早期识别[J]. , 2014, 50(17): 69-76. |
[15] | 严保康;周凤星. 基于相干累积量分段正交匹配追踪方法的轴承早期故障稀疏特征提取[J]. , 2014, 50(13): 88-96. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||