Acoustic Source Location Method for Substation Based on Non-redundant Fourth-order Cumulant

被引：0
|
作者：
Zhang Y. [1 ]
Luo L. [1 ]
Chen J. [2 ]
Sheng G. [1 ]
Jiang X. [1 ]
机构：
[1] Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai
[2] Qingpu Power Supply Company, State Grid Shanghai Electric Power Company, Shanghai
来源：
Gaodianya Jishu/High Voltage Engineering | 2022年 / 48卷 / 01期
基金：
国家重点研发计划;
关键词：
Acoustic source localization; Array expansion; Fourth-order cumulant; Non-redundant; Substation;
D O I：
10.13336/j.1003-6520.hve.20210782
中图分类号：
学科分类号：
摘要：
The performance of abnormal acoustic source location technology based on acoustic sensor array is not only related to the processing algorithm, but also dependent on the sensor array model. Increasing the number of sensors to improve the estimation performance often leads to the increase of cost. In this paper, an array virtual expansion method based on the non-redundant fourth-order cumulant of a uniform planar array is proposed. This method can be adopted to improve the performance of the array without changing the hardware design. Compared with the low-order cumulants, the high-order cumulants can increase the number of sensors and the number of distinguishable sources by extending the array virtually, and can improve the location estimation performance. In addition, since the high-order cumulants only contain the information of non-Gaussian components, the expanded array shows better anti-Gaussian-noise performance. Moreover, the complexity of the algorithm is reduced and the real-time performance of the localization method is improved by removing the redundancy and dimension reduction of the cumulant matrix. The simulation and field test results show that the proposed method can be adopted to locate more acoustic source signals than the number of real sensors. The localization accuracy is significantly improved when the signal-to-noise ratio is low, the resolution of the localization method is also improved, and the calculation amount of the algorithm can be effectively reduced. © 2022, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
引用
收藏
页码：75 / 83
页数：8
相关论文
共 23 条
  • [1] LIU Yunpeng, WANG Bowen, YUE Haotian, Et al., Identification of transformer bias voiceprint based on 50 Hz frequency multiplication cepstrum coefficients and gated recurrent unit, Proceedings of the CSEE, 40, 14, pp. 4681-4694, (2020)
  • [2] SUN Shuguang, YU Han, DU Taihang, Et al., Vibration and acoustic joint fault diagnosis of conventional circuit breaker based on multi-feature fusion and improved QPSO-RVM, Transactions of China Electrotechnical Society, 32, 19, pp. 107-117, (2017)
  • [3] LI Xiuguang, WU Xutao, SHI Yuhang, Et al., Charged detection system of GIS mechanical fault based on the acoustical imaging, High Voltage Apparatus, 55, 5, pp. 42-46, (2019)
  • [4] HAN X, JIANG J, ZHANG C H, Et al., Research on quality problems management of electric power equipment based on knowledge-data fusion method, IET Generation, Transmission & Distribution, 14, 5, pp. 745-751, (2020)
  • [5] ZHANG Jiangong, WANG Yanzhao, CHEN Yuchao, Et al., Measurement for sound power of a UHV transformer based on a combined vibration and sound pressure method, High Voltage Engineering, 45, 6, pp. 1843-1850, (2019)
  • [6] SHI Yuhang, JI Shengchang, ZHANG Fan, Et al., Research on vibration morphology characteristics of transformer tank surface, Transactions of China Electrotechnical Society, 34, 5, pp. 1088-1095, (2019)
  • [7] ZHANG Zhongyuan, LUO Shihao, YUE Haotian, Et al., Pattern recognition of acoustic signals of transformer core based on Mel-spectrum and CNN, High Voltage Engineering, 46, 2, pp. 413-423, (2020)
  • [8] ZHANG Yao, LUO Lingen, WANG Hui, Et al., Method of locating abnormal acoustic source of substation equipment based on MPSO-MLE, High Voltage Engineering, 46, 9, pp. 3145-3153, (2020)
  • [9] WANG Guoli, DU Fei, PAN Wei, Et al., Modified RSS algorithm for ultrasonic array applied to PD direction in transformer, High Voltage Engineering, 45, 8, pp. 2509-2514, (2019)
  • [10] ZHANG Zhongyuan, YUE Haotian, WANG Bowen, Et al., Pattern recognition of partial discharge ultrasonic signal based on similar matrix BSS and deep learning CNN, Power System Technology, 43, 6, pp. 1900-1906, (2019)
123