[1]ZHAI Xueming,GUO Jia,ZHAI Yujia.Audio detection of transformer partial discharge fault based on SPNCC and one-dimensional dual-channel CNN-LSTM[J].CAAI Transactions on Intelligent Systems,2023,18(3):534-543.[doi:10.11992/tis.202110036]
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Audio detection of transformer partial discharge fault based on SPNCC and one-dimensional dual-channel CNN-LSTM

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 534-543 Column: 学术论文—智能系统 Public date: 2023-07-05
Title:
Audio detection of transformer partial discharge fault based on SPNCC and one-dimensional dual-channel CNN-LSTM
Author(s):
ZHAI Xueming1;  GUO Jia1;  ZHAI Yujia2
1. School of control and computer engineering, North China Electric Power University, Baoding 071003, China;
2. State Grid Shijiazhuang Electric Power Company, Shijiazhuang 050000, China
Keywords:
transformer partial discharge; transformer fault detection; smart grid; wavelet packet decomposition; simple power normalized cepstral coefficient; Gammatone filter; convolutional neural network; long short-term memory
CLC:
TP183；TM41
DOI:
10.11992/tis.202110036
Abstract:
The sound signals generated during transformer operation contain abundant state information, which can be used as an important basis for transformer fault diagnosis. In order to improve the audio diagnosis effect of transformer fault, firstly, the sound signals collected in the field during transformer operation are divided into working environment noise signals, normal working audio signals and partial discharge fault audio signals. Then, the non-stationary environmental noise and short-term stationary environmental noise signals in the original audio signals are removed by the methods of convolutional neural network classification, wavelet packet decomposition and Butterworth band-pass filtering. And a one-dimensional two-channel convolutional neural network-long short-term memory (CNN-LSTM) model for transformer partial discharge fault identification is established based on the simple power normalized cepstral coefficient (SPNCC) feature. By collecting sound signals from a 500 kV substation transformer operation under different natural environments and the fault simulation experiments, the feasibility of the proposed partial discharge fault recognition model is verified. Compared with traditional audio fault diagnosis methods, the proposed model has a faster convergence speed, better fault identification accuracy and fault classification accuracy.
References:
[1] 肖飞, 叶康, 邓祥力, 等. 基于最优编码集及智能状态估计的电网故障诊断方法[J]. 电力系统保护与控制, 2021, 49(2): 89–97
XIAO Fei, YE Kang, DENG Xiangli, et al. A fault diagnosis method of a power grid based on an optimal coding set and intelligent state estimation[J]. Power system protection and control, 2021, 49(2): 89–97
[2] 肖舒严, 王强钢, 周念成. 基于稳健加权总体最小二乘的变压器局部放电定位[J]. 电力自动化设备, 2020, 40(8): 203–215
XIAO Shuyan, WANG Qianggang, ZHOU Niancheng. Partial discharge location of transformer based on robust weighted total least square[J]. Electric power automation equipment, 2020, 40(8): 203–215
[3] 张重远, 岳浩天, 王博闻, 等. 基于相似矩阵盲源分离与卷积神经网络的局部放电超声信号深度学习模式识别方法[J]. 电网技术, 2019, 43(6): 1900–1907
ZHANG Zhongyuan, YUE Haotian, WANG Bowen, et al. Pattern recognition of partial discharge ultrasonic signal based on similar matrix BSS and deep learning CNN[J]. Power system technology, 2019, 43(6): 1900–1907
[4] ZHOU Zhengqin, XIAO Li, NIE Dexin, et al. Validity evaluation method of DGA monitoring sensor in power transformer based on chaos theory[C]//2018 IEEE Conference on Electrical Insulation and Dielectric Phenomena. Cancun: IEEE, 2018: 402?405.
[5] 李建文. 基于暂态对地电压法的开关柜内部绝缘缺陷检测研究[D]. 镇江: 江苏大学, 2019.
LI Jianwen. Research on detection of internal insulation defects of switchgear based on transient earth voltage method[D]. Zhenjiang: Jiangsu University, 2019.
[6] 李沐, 冯新岩, 蔄晓琨. 基于TDOA和TS-PSO的变压器特高频局部放电空间定位方法[J]. 中国电机工程学报, 2019, 39(6): 1834–1842,1879
LI Mu, FENG Xinyan, MAN Xiaokun. A transformer partial discharge UHF localization method based on TDOA and TS-PSO[J]. Proceedings of the CSEE, 2019, 39(6): 1834–1842,1879
[7] 杨壮, 周渠, 赵耀洪, 等. 基于人工神经网络和多频超声波检测技术的变压器油界面张力预测[J]. 高电压技术, 2019, 45(10): 3343–3349
YANG Zhuang, ZHOU Qu, ZHAO Yaohong, et al. Prediction of interfacial tension of transformer oil based on artificial neural network and multi-frequency ultrasonic testing technology[J]. High voltage engineering, 2019, 45(10): 3343–3349
[8] 李恩文, 王力农, 宋斌, 等. 基于混沌序列的变压器油色谱数据并行聚类分析[J]. 电工技术学报, 2019, 34(24): 5104–5114
LI Enwen, WANG Linong, SONG Bin, et al. Parallel clustering analysis of dissolved gas analysis data based on Chaotic sequences replacement[J]. Transactions of China electrotechnical society, 2019, 34(24): 5104–5114
[9] GHONEIM S S M. Intelligent prediction of transformer faults and severities based on dissolved gas analysis integrated with thermodynamics theory[J]. IET science, measurement & technology, 2018, 12(3): 388–394.
[10] LIU Hongshun, JI Liang, HAN Mingming, et al. Analysis on amplitude frequency characteristics of GIS shell TEV in substation with HRPC[C]//2018 China International Conference on Electricity Distribution. Tianjin: IEEE, 2018: 1572?1575.
[11] 耿琪深, 王丰华, 金霄. 基于Gammatone滤波器倒谱系数与鲸鱼算法优化随机森林的干式变压器机械故障声音诊断[J]. 电力自动化设备, 2020, 40(8): 191–196,224,197
GENG Qishen, WANG Fenghua, JIN Xiao. Mechanical fault sound diagnosis based on GFCC and random forest optimized by whale algorithm for dry type transformer[J]. Electric power automation equipment, 2020, 40(8): 191–196,224,197
[12] 刘云鹏, 王博闻, 岳浩天, 等. 基于50Hz倍频倒谱系数与门控循环单元的变压器偏磁声纹识别[J]. 中国电机工程学报, 2020, 40(14): 4681–4694,4746
LIU Yunpeng, WANG Bowen, YUE Haotian, et al. Identification of transformer bias voiceprint based on 50Hz frequency multiplication cepstrum coefficients and gated recurrent unit[J]. Proceedings of the CSEE, 2020, 40(14): 4681–4694,4746
[13] 邵宇鹰, 王枭, 彭鹏, 等. 基于声场听觉感知的变压器故障诊断方法研究[J]. 中国测试, 2021, 47(3): 92–97
SHAO Yuying, WANG Xiao, PENG Peng, et al. Research on transformer fault diagnosis method based on auditory perception of sound field[J]. China measurement & test, 2021, 47(3): 92–97
[14] 李正明, 钱露先, 李加彬. 基于统计特征与概率神经网络的变压器局部放电类型识别[J]. 电力系统保护与控制, 2018, 46(13): 55–60
LI Zhengming, QIAN Luxian, LI Jiabin. Type recognition of partial discharge in power transformer based on statistical characteristics and PNN[J]. Power system protection and control, 2018, 46(13): 55–60
[15] 吴晓文, 周年光, 彭继文, 等. 电力变压器噪声特性与相关因素分析[J]. 电力科学与技术学报, 2018, 33(3): 81–85,146
WU Xiaowen, ZHOU Nianguang, PENG Jiwen, et al. Noise characteristic and relevant factors analysis of power transformers[J]. Journal of electric power science and technology, 2018, 33(3): 81–85,146
[16] 翟海清, 陈敏懋. 新型铁心绑扎机的研制[J]. 变压器, 2013, 50(1): 23–24
ZHAI Haiqing, CHEN Minmao. Development of a new type of iron core binding machine[J]. Transformer, 2013, 50(1): 23–24
[17] 高沛, 王丰华, 苏磊, 等. 直流偏磁下电力变压器的振动特性[J]. 电网技术, 2014, 38(6): 1536–1541
GAO Pei, WANG Fenghua, SU Lei, et al. Analysis on vibration characteristics of power transformer under DC bias[J]. Power system technology, 2014, 38(6): 1536–1541
[18] 王格万, 潘超, 郑迤丹, 等. 变压器直流扰动下励磁谐波与铁心饱和失稳研究[J]. 电力系统保护与控制, 2019, 47(9): 49–55
WANG Gewan, PAN Chao, ZHENG Yidan, et al. Research on excitation harmonics and core saturation instability of transformer under DC disturbance[J]. Power system protection and control, 2019, 47(9): 49–55
[19] 张重远, 罗世豪, 岳浩天, 等. 基于Mel时频谱-卷积神经网络的变压器铁芯声纹模式识别方法[J]. 高电压技术, 2020, 46(2): 413–423
ZHANG Zhongyuan, LUO Shihao, YUE Haotian, et al. Pattern recognition of acoustic signals of transformer core based on mel-spectrum and CNN[J]. High voltage engineering, 2020, 46(2): 413–423
[20] 刘云鹏, 罗世豪, 王博闻, 等. 基于Mel时频谱-卷积神经网络的变压器铁芯夹件松动故障声纹模式识别[J]. 华北电力大学学报(自然科学版), 2020, 47(6): 52–60,67
LIU Yunpeng, LUO Shihao, WANG Bowen, et al. Voiceprint recognition of transformer core clamp looseness fault by mel-spectrum and convolutional neural network[J]. Journal of North China electric Power University (natural science edition), 2020, 47(6): 52–60,67
[21] BAIG F, BEG S, KHAN M F. Speaker recognition based appliances remote control for severely disabled, low vision and old aged persons[J]. INAE letters, 2018, 3(1): 1–9.
[22] SHI Xiaoyuan, YANG Haiyan, ZHOU Ping. Robust speaker recognition based on improved GFCC[C]//2016 2nd IEEE International Conference on Computer and Communications. Chengdu: IEEE, 2017: 1927-1931.
[23] KIM C, STERN R M. Power-normalized cepstral coefficients (PNCC) for robust speech recognition[C]//2012 IEEE International Conference on Acoustics, Speech and Signal Processing. Kyoto: IEEE, 2012: 4101?4104.
[24] 翟永杰, 杨旭, 赵振兵, 等. 融合共现推理的Faster R-CNN输电线路金具检测[J]. 智能系统学报, 2021, 16(2): 237–246
ZHAI Yongjie, YANG Xu, ZHAO Zhenbing, et al. Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection[J]. CAAI transactions on intelligent systems, 2021, 16(2): 237–246
[25] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735–1780.
[26] 张勇, 高大林, 巩敦卫, 等. 用于关系抽取的注意力图长短时记忆神经网络[J]. 智能系统学报, 2021, 16(3): 518–527
ZHANG Yong, GAO Dalin, GONG Dunwei, et al. Attention graph long short-term memory neural network for relation extraction[J]. CAAI transactions on intelligent systems, 2021, 16(3): 518–527
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