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基于超声波声压衰减效应的局部放电源定位与强度标定

王玉龙 张晓虹 李丽丽 高俊国 郭宁 程成

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基于超声波声压衰减效应的局部放电源定位与强度标定

王玉龙, 张晓虹, 李丽丽, 高俊国, 郭宁, 程成

Localization and intensity calibration of partial discharge based on attenuation effect of ultrasonic sound pressure

Wang Yu-Long, Zhang Xiao-Hong, Li Li-Li, Gao Jun-Guo, Guo Ning, Cheng Cheng
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  • 局部放电是导致电力设备绝缘劣化或击穿的重要原因之一. 为此, 结合即到达时差法定位原理, 在广义互相关法的基础上, 引入量子遗传算法对局部放电源进行精准定位. 而后以声波传播损耗、反射及折射现象导致的声压衰减效应为研究切入点, 首次建立局部放电源超声波信号标定的数学模型. 结果表明: 在针-板放电模型中, 利用量子遗传算法计算的局部放电源较为精准, 其最大偏差为(0.27 ± 0.13) cm, 与遗传算法、模拟退火算法、粒子群优化算法以及广义互相关法相比, 其定位精度分别提高了33.57%, 41.51%, 32.12%以及87.26%. 与此同时, 由于声压衰减效应, 当测量得到的超声信号电压幅值相同时, 随着测试距离增大, 放电源处的视在放电量逐渐增加. 若测试距离为37.80 cm时, 局部放电源的视在放电量为633.83 pC, 与7.00 cm相比, 放电强度增大了28.51%. 局部放电源的放电曲线与标定拟合曲线几乎完全重合, 验证了放电源放电程度标定模型的准确性.
    In the insulation system of power equipment, the partial discharge (PD) of short period does not cause the insulation to produce the penetrating breakdown, however the long-term PD of is one of the important causes of local deterioration, and even breakdown in dielectric. Therefore, it is very important to study the location of PD source and the calibration of discharge intensity. To achieve this, in this paper we take the needle-plate discharge model for example and go through the following steps respectively. Firstly, combined with the positive correlation between the ultrasonic signal and the apparent discharge magnitude in the process of PD, the ultrasonic method to detect partial discharge can be implemented. Then, based on the principle of time difference of arrival method (TDOAM), the accuracy of location is analyzed by using quantum genetic algorithm (QGA), genetic algorithm (GA), simulated annealing algorithm (SAA), particle swarm optimization (PSO) and generalized cross correlation method (GCC), respectively. And thus, starting from the study of the attenuation effect of sound pressure caused by the propagation loss, reflection and refraction of acoustic wave, the calibration model of PD intensity is established for the first time after determining the location of PD source with high precision. Some important findings are extracted from simulations and experimental results. First, the localization algorithm of PD source with high precision is observed. The localization of PD source by means of QGA is the most accurate, with maximum deviation of (0.27 ± 0.13) cm. Comparing with GA, SAA, PSO and GCC, the accuracy of location is improved by 33.57%, 41.51%, 32.11% and 87.26%, respectively. Second, due to the attenuation effect of sound pressure, when the measured voltage amplitude of ultrasonic signal is the same, the apparent discharge magnitude of PD source gradually increases with the test distance increasing. When the test distance is 37.80 cm, the apparent discharge magnitude of PD source is 633.83 pC, which increases by 28.51% compared with 7.00 cm. Moreover, simulation results and measurement results are compared with each other and they are well consistent. The discharge curve almost coincides with the calibration fitting curve of PD source when the test distance is 7.00 cm. Finally, it is concluded that the discharge intensity calibration model of PD source is accurate, which is of great significance in evaluating the extent of insulation damage.
      通信作者: 张晓虹, x_hzhang2002@hrbust.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 51577045)和广东省普通高校青年创新人才项目(批准号: 2020KQNCX117)资助的课题
      Corresponding author: Zhang Xiao-Hong, x_hzhang2002@hrbust.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 51577045) and the Youth Innovation Talent Project of Universities of Guangdong Province, China (Grant No. 2020KQNCX117)
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  • 图 1  超声波检测试验系统电路图(T1, 隔离变压器; T2, 调压器; C1, L1, 低压低通π型滤波器; T3, 高压实验变压器; C2, L2, 高压低通滤波器; CK, 耦合电容器; Zin, 检测阻抗; T, 油箱; S, 压电传感器; AMP, 前置放大器; DAQ, 数据采集卡)

    Fig. 1.  Test system schematic diagram of ultrasonic testing (T1, isolating transformer; T2, voltage regulator; C1, L1, low-voltage low-pass π filter; T3, high voltage test transformer; C2, L2, high-voltage low-pass filter; CK, coupling capacitor; Zin, detection impedance; T, Tank; S, piezoelectric sensor; AMP, preamplifier; DAQ, data acquisition card).

    图 2  针-板放电模型结构示意图(1, 高压引线; 2, 聚乙烯试验板; 3, 聚四氟乙烯支架; 4, 铜电极; 5, 电缆油)

    Fig. 2.  Schematic diagram of needle-plate discharge model (1, high voltage wire; 2, polyethylene sample; 3, support frame of polytetrafluoroethylene; 4, copper electrode; 5, cable oil).

    图 3  油箱中放电源及超声波传感器的位置图

    Fig. 3.  Location illustration of PD source and ultrasonic sensors in the oil tank.

    图 4  TDOAM中的超声时差示意图

    Fig. 4.  Schematic diagram of ultrasonic time difference in TDOAM.

    图 5  脉冲电流法的校正曲线

    Fig. 5.  Calibration curve of pulse current method.

    图 6  不同算法下局部放电定位的平均绝对误差变化

    Fig. 6.  Average absolute errors of PD location under different algorithms.

    图 7  不同算法下局部放电定位的平均误差变化 (a) 平均绝对误差; (b) 平均最大偏差和综合误差

    Fig. 7.  Average errors of PD location under different algorithms: (a) εrx, εry and εrz; (b) Dmax and ΔR.

    图 8  不同绝缘纸厚度的电压幅值与放电量拟合曲线 (a) 2 mm; (b) 3 mm; (c) 4 mm

    Fig. 8.  Fitting curves of voltage amplitude and discharge amplitudes at different thickness of insulating papers: (a) 2 mm; (b) 3 mm; (c) 4 mm.

    图 9  不同测量距离下视在放电量与电压的关系

    Fig. 9.  Relationship between apparent charge and voltage at different measuring distances.

    表 1  局部放电的超声定位精度比较

    Table 1.  Comparison of ultrasonic location accuracy of PD.

    使用算法研究对象综合距离误差ΔR/cm最大偏差Dmax/cm
    广义互相关算法油箱体内3.94.4
    时延法变压器绝缘0.80.6
    基于高斯-牛顿迭代的等值声速修正算法1.71.6
    粒子群优化算法2.21.9
    遗传算法1.82.0
    多平台测向与全局搜索的阵列定位的结合三电容放电管模型7.86.0
    基于测向线公垂线中点的局部放电相控超声几何定位算法13.910.3
    Chan算法电缆绝缘9.012.0
    下载: 导出CSV

    表 2  QGA的程序过程

    Table 2.  Procedures of QGA.

    过程程序
    种群初
    始化
    $Q\left( t \right) = \left| { {\psi _{q_j^0} } } \right\rangle = \displaystyle\sum\limits_{k = 1}^{ {2^m} } {\dfrac{1}{ {\sqrt { {2^m} } } }\left| { {S_k} } \right\rangle }$
    预设进
    化条件
    Cmax, t, N, Pmax, Pc
    算法
    实现
    For t = 1, 2, 3, ···, Cmax
     for i = 1, 2, ···, N
     ${P_i} = {f_i}\Big/\sum\limits_{i = 1}^N { {f_i} }$
     $\quad P(t) = \left\{ {p_1^t, p_2^t, \cdots, p_n^t} \right\},$
     ${P_c} \!=\! \left\{\!\!\!\! \begin{array}{l}\dfrac{ { { {P_{c\max} } + {P_{\min} } } } }{ {1 \!+\! \exp\left\{ {A\left[ {\dfrac{ {2(f-f')} }{ { {f_{\max} } - {f_{\rm{avg} } } } } } \right]} \right\} } } \!+\! {P_{c\min} }, ~{f \!\geqslant\! {f_{\rm{avg} } } } \\ {P_{c\max} }, \qquad\quad\qquad\qquad\quad\qquad\qquad{f \!\leqslant\! {f_{\rm{avg} } } } \end{array} \right.$
     ${P_m} \!=\! \left\{\!\!\!\! \begin{array}{l} \dfrac{ { { {P_{m\max} } - {P_{\min} } } } }{ {1 \!+\! \exp\left\{ {A\left[ {\dfrac{ {2( {f'' - f'})} }{ { {f_{\max} } - {f_{\rm{avg} } } } } } \right]} \right\} } } \!+\! {P_{m\min} }, ~~{f'' \geqslant {f_{\rm{avg} } } } \\ {P_{m\max} },\;\;\; \qquad\quad\qquad\qquad\qquad\qquad\quad{f'' \leqslant {f_{\rm{avg} } } } \end{array} \right.$
     ${F_{t + 1} }({U({x, y, z, {v_{\rm{e} } }})}) \!=\! {C_{t\max} } \!-\! {U_t}( {x, y, z, {v_{\rm{e} } }})$
     $X_i \;\& \; x_{ {\rm best}, i}\; \& \; f(x) > f(x_{ {\rm best}, i}) \; \& \; \Delta \theta_i$
     S(αi, βi);
     end
    end
    S(αi, βi); P(t); X;
    下载: 导出CSV

    表 3  量子旋转门的调整策略

    Table 3.  Adjustment strategies of quantum rotation gates.

    xixbest,if (x) > f (xbest,i)ΔθiS(αi, βi)
    αi βi > 0αi βi < 0αi = 0βi = 0
    00false00000
    00true00000
    01false0.01π+1–10 ± 1
    01true0.01π–1+1 ± 10
    10false0.01π–1+1 ± 10
    10true0.01π+1–10 ± 1
    11false00000
    11true00000
    下载: 导出CSV

    表 4  局部放电定位的算法参数

    Table 4.  Algorithm parameters of PD localization.

    算法参数数值
    QGA群体数量40
    最大遗传次数200
    GA群体数量40
    最大遗传次数200
    重组概率0.9
    变异概率0.01
    SAA初始温度10
    最终温度0.0001
    衰减系数0.8
    冷却新状态迭代次数1000
    PSO种群大小40
    学习因子2
    初始惯性权值0.9
    原始粒子群1
    迭代次数100
    下载: 导出CSV

    表 5  不同算法的局部放电定位

    Table 5.  The PD location of different algorithms.

    算法位置
    实验组1 (12, 14, 6) cm实验组2 (14, 10, 6) cm实验组3 (15, 11, 6) cm实验组4 (16, 12, 6) cm
    QGA(11.79, 13.61, 5.78)(13.78, 9.88, 6.06)(14.88, 10.86, 5.90)(15.82, 11.84, 5.88)
    GA(12.33, 14.24, 5.73)(13.62, 10.22, 6.16)(14.65, 11.24, 6.22)(16.34, 12.30, 6.24)
    SAA(11.58, 13.41, 5.78)(13.66, 10.32, 6.24)(15.34, 11.22, 6.20)(16.32, 12.28, 6.26)
    PSO(12.12, 14.21, 6.15)(14.32, 9.72, 5.84)(15.42, 11.28, 6.30)(16.42, 12.34, 6.32)
    GCC(13.38, 15.06, 7.42)(15.51, 9.62, 6.94)(15.71, 8.32, 7.24)(14.76, 10.31, 7.13)
    下载: 导出CSV

    表 6  系统灵敏度和相关系数的变化

    Table 6.  Change of system sensitivity and correlation coefficients.

    电缆纸厚度/mm系统灵敏度相关系数
    214.910.99
    314.370.99
    414.840.99
    下载: 导出CSV

    表 7  局部放电的线性系数

    Table 7.  Linear coefficients of PD.

    线性系数K0K1K2K3K4
    数值5.012.982.932.862.95
    下载: 导出CSV
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    李丹, 胡海云 2014 物理学报 63 117701Google Scholar

    Li D, Hu H Y 2014 Acta Phys. Sin. 63 117701Google Scholar

    [2]

    Lü Z P, Rowland S M, Chen S Y, Zheng H L 2018 IEEE Trans. Dielectr. Electr. Insul. 25 1999Google Scholar

    [3]

    赵法强, 唐明, 郭飞飞 2020 电线电缆 63 20Google Scholar

    Zhao F Q, Tang M, Guo F F 2020 Electric Wire & Cable 63 20Google Scholar

    [4]

    Romano P, Presti G, Imburgia A, Candela R 2018 IEEE Electr. Insul. M. 34 32Google Scholar

    [5]

    李军浩, 韩旭涛, 刘泽辉, 李彦明 2015 高电压技术 41 2583Google Scholar

    Li J H, Han X T, Liu Z H, Li Y M 2015 High Voltage Eng. 41 2583Google Scholar

    [6]

    Heredia L C C, Mor A R 2019 Int. J. Electr. Power Energy Syst. 107 224Google Scholar

    [7]

    Callender G, Golosnoy I O, Rapisarda P, Lewin P L 2018 J. Phys. D: Appl. Phys. 51 125601Google Scholar

    [8]

    杜劲超, 陈伟根, 张知先, 杨贤 2020 高电压技术 46 2185Google Scholar

    Du J C, Chen W G, Zhang Z X, Yang X 2020 High Voltage Eng. 46 2185Google Scholar

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    夏睿, 谭笑, 陈杰, 刘洋, 胡丽斌, 李陈莹, 王伟 2020 绝缘材料 53 95Google Scholar

    Xia R, Tan X, Chen J, Liu J, Hu L B, Li C Y, Wang W 2020 Insulating Mater. 53 95Google Scholar

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    Iorkyase E T, Tachtatzis C, Atkinson R C, Glover I A 2015 Loughborough Antennas & Propagation Conference UK, Loughborough, November 2−3, 2015 p1

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    Lu Y, Lou Z D 1999 Trans. Chin. Electrotech. Soc. 14 51Google Scholar

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    Li Y Q, Lu F C, Xie H L, Yin M 2004 International Conference on Power System Technology Singapore, Singapore, November 21–24, 2004 p1371

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    吴治国 2007 硕士学位论文 (武汉: 华中科技大学)

    Wu Z G 2007 M. S. Thesis (Wuhan: Huazhong University of Science & Technology) (in Chinese)

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    Veloso G F C, da Silva L E B, Noronha I, Lambert-Torres G 2008 IEEE International Symposium on Industrial Electronics Cambridge, UK, June 30–July 2, 2008 p1003

    [18]

    Tang L J, Luo R C, Deng M, Su J 2008 IEEE Trans. Dielectr. Electr. Insul. 15 492Google Scholar

    [19]

    张丽君, 谢庆, 李菱, 律方成 2012 高压电器 48 30Google Scholar

    Zhang L J, Xie Q, Li L, Lv F C 2012 High Voltage Apparatus 48 30Google Scholar

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    瞿磊 2020 硕士学位论文 (上海: 上海电机学院)

    Qu L 2020 M. S. Thesis (Shanghai: Shanghai Dian Ji university) (in Chinese)

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    Zhu Y C, Zhou L, Xu H S 2020 Neural Comput. Appl. 32 1775Google Scholar

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出版历程
  • 收稿日期:  2020-10-17
  • 修回日期:  2020-11-27
  • 上网日期:  2021-04-19
  • 刊出日期:  2021-05-05

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