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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.
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Keywords:
- partial discharge/
- localization of ultrasonic method/
- discharge intensity/
- mathematical model
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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 3.9 4.4 时延法 变压器绝缘 0.8 0.6 基于高斯-牛顿迭代的等值声速修正算法 1.7 1.6 粒子群优化算法 2.2 1.9 遗传算法 1.8 2.0 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 7.8 6.0 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 13.9 10.3 Chan算法 电缆绝缘 9.0 12.0 
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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Fort= 1, 2, 3, ···,Cmax fori= 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; DownLoad:
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xi xbest,i f(x) >f(xbest,i) Δθi S(αi,βi) αiβi> 0 αiβi< 0 αi= 0 βi= 0 0 0 false 0 0 0 0 0 0 0 true 0 0 0 0 0 0 1 false 0.01π +1 –1 0 ± 1 0 1 true 0.01π –1 +1 ± 1 0 1 0 false 0.01π –1 +1 ± 1 0 1 0 true 0.01π +1 –1 0 ± 1 1 1 false 0 0 0 0 0 1 1 true 0 0 0 0 0 DownLoad:
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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 DownLoad:
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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) DownLoad:
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[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35]
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