UHF Partial Discharge Localization Methodology Based on Generalized Regression Neural Network

doi:10.11930/j.issn.1004-9649.202005016

Abstract

Abstract: Partial discharge (PD) detection and localization is an important means for condition monitoring and diagnosis of power equipment. The existing time-difference based ultra-high frequency (UHF) PD localization techniques are limited in application due to their high costs. A novel PD localization method is proposed based on generalized regression neural network (GRNN) and received signal strength indicator (RSSI) fingerprint, which consists of two stages. In the off-line stage of algorithm, a RSSI fingerprint map is built. In the on-line stage, the GRNN is used to calculate the position of the PD source. The field testing shows that the proposed UHF PD localization method has an average localization error of 0.51 m, and a cumulative probability of 81.6% for the localization error of less than 1 m. Compared to the minimum mean square error (MSE) of the Cramér-Rao lower bound (CRLB), which is based on RSSI log normal shadowing model positioning method, the cumulative probability of the GRNN localization error with the mean square error less than 0.6 m² is 66.7%, which is better than CRLB. The proposed method overcomes the shortcomings of low positioning accuracy and high costs of the traditional methods, and has the characteristics of low hardware cost and good environmental adaptability.

Key words: partial discharge, RSSI fingerprint, GRNN, log normal shadowing model, positioning technology

YU Qichen, LUO Lingen, WU Fan, SHENG Gehao, JIANG Xiuchen. UHF Partial Discharge Localization Methodology Based on Generalized Regression Neural Network[J]. Electric Power, 2021, 54(2): 11-17.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202005016

https://www.electricpower.com.cn/EN/Y2021/V54/I2/11

References

[1] AREFIFAR S A, MOHAMED Y A R I, EL-FOULY T H M. Comprehensive operational planning framework for self-healing control actions in smart distribution grids[J]. IEEE Transactions on Power Systems, 2013, 28(4): 4192-4200.
[2] LI C R, MA G M, QI B, et al. Condition monitoring and diagnosis of high-voltage equipment in China-recent progress[J]. IEEE Electrical Insulation Magazine, 2013, 29(5): 71-78.
[3] 李军浩, 韩旭涛, 刘泽辉, 等. 电气设备局部放电检测技术述评[J]. 高电压技术, 2015, 41(8): 2583-2601
LI Junhao, HAN Xutao, LIU Zehui, et al. Review on partial discharge measurement technology of electrical equipment[J]. High Voltage Engineering, 2015, 41(8): 2583-2601
[4] 侯慧娟, 盛戈皞, 苗培青, 等. 基于超高频电磁波的变电站局部放电空间定位[J]. 高电压技术, 2012, 38(6): 1334-1340
HOU Huijuan, SHENG Gehao, MIAO Peiqing, et al. Partial discharge location based on radio frequency antenna array in substation[J]. High Voltage Engineering, 2012, 38(6): 1334-1340
[5] SINAGA H H, PHUNG B T, AO P L, et al. Partial discharge localization in transformers using UHF sensors[C]//2011 Electrical Insulation Conference (EIC). Annapolis, MD, USA. IEEE, 2011: 64-68.
[6] YIN J H, WAN Q, YANG S W, et al. A simple and accurate TDOA-AOA localization method using two stations[J]. IEEE Signal Processing Letters, 2016, 23(1): 144-148.
[7] 杨森, 熊俊, 郑服利, 等. 基于特高频无线智能传感器的局部放电定位法[J]. 电气自动化, 2017, 39(2): 110-112, 115
YANG Sen, XIONG Jun, ZHENG Fuli, et al. Partial discharge localization based on UHF wireless smart sensors[J]. Electrical Automation, 2017, 39(2): 110-112, 115
[8] 韩宝国, 马驰, 李静鹏, 等. 基于DTCWT与LLE算法的变压器局部放电特高频信号特征参数提取方法[J]. 电力系统保护与控制, 2019, 47(20): 65-72
HAN Baoguo, MA Chi, LI Jingpeng, et al. A feature parameters extraction method of PD UHF signal based on DTCWT and LLE algorithm[J]. Power System Protection and Control, 2019, 47(20): 65-72
[9] 陶为戈, 朱昳华, 贾子彦. 基于RSSI混合滤波和最小二乘参数估计的测距算法[J]. 传感技术学报, 2012, 25(12): 1748-1753
TAO Weige, ZHU Yihua, JIA Ziyan. A distance measurement algorithm based on RSSI hybrid filter and least square estimation[J]. Chinese Journal of Sensors and Actuators, 2012, 25(12): 1748-1753
[10] ASLAM M W, ZHU Z C, NANDI A K. Automatic modulation classification using combination of genetic programming and KNN[J]. IEEE Transactions on Wireless Communications, 2012, 11(8): 2742-2750.
[11] CHEN F C. Back-propagation neural networks for nonlinear self-tuning adaptive control[J]. IEEE Control Systems Magazine, 1990, 10(3): 44-48.
[12] JIN Y Y, SOH W S, WONG W C. Indoor localization with channel impulse response based fingerprint and nonparametric regression[J]. IEEE Transactions on Wireless Communications, 2010, 9(3): 1120-1127.
[13] 陈伟根, 奚红娟, 苏小平, 等. 广义回归神经网络在变压器绕组热点温度预测中的应用[J]. 高电压技术, 2012, 38(1): 16-21
CHEN Weigen, XI Hongjuan, SU Xiaoping, et al. Application of generalized regression neural network to transformer winding hot spot temperature forecasting[J]. High Voltage Engineering, 2012, 38(1): 16-21
[14] NOSE-FILHO K, LOTUFO A D P, MINUSSI C R. Short-term multinodal load forecasting using a modified general regression neural network[J]. IEEE Transactions on Power Delivery, 2011, 26(4): 2862-2869.
[15] 李冬辉, 尹海燕, 郑博文. 基于MFOA-GRNN模型的年电力负荷预测[J]. 电网技术, 2018, 42(2): 585-590
LI Donghui, YIN Haiyan, ZHENG Bowen. An annual load forecasting model based on generalized regression neural network with multi-swarm fruit fly optimization algorithm[J]. Power System Technology, 2018, 42(2): 585-590
[16] CARDOSO G, ROLIM J, ZURN H H. Application of neural network modules to electric power system fault section estimation[J]. IEEE Power Engineering Society General Meeting, 2004, 115(1): 1034-1041.
[17] SPECHT D F. A general regression neural network[J]. IEEE Transactions on Neural Networks, 1991, 2(6): 568-576.
[18] PARZEN E. On estimation of a probability density function and mode[J]. The Annals of Mathematical Statistics, 1962, 33(3): 1065-1076.
[19] TAYLOR J H. The Cramer-Rao estimation error lower bound computation for deterministic nonlinear systems[J]. 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, 1978: 1178-1181.
[20] GEZICI S. A survey on wireless position estimation[J]. Wireless Personal Communications, 2008, 44(3): 263-282.