Equipment Fault Acoustic Source Direction of Arrival Estimation with Microphone Arrays Using SRP-PHAT Method

被引：0

作者：

Chen, Jingde ^{[1
]}

Shen, Xiaofeng ^{[1
]}

Lu, Minan ^{[1
]}

Wu, Jijian ^{[1
]}

Zhou, Nan ^{[2
]}

Luo, Lingen ^{[2
]}

机构：

[1] Qingpu Power Supply Co, State Grid Shanghai Elect Power Co, Shanghai, Peoples R China

[2] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai, Peoples R China

来源：

2020 5TH ASIA CONFERENCE ON POWER AND ELECTRICAL ENGINEERING (ACPEE 2020) | 2020年

关键词：

insulation fault; insulation deterioration diagnosis; acoustic sensor array; direction of arrival estimation; steered response power; NEURAL-NETWORKS; LOCALIZATION;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The localization and detection of equipment fault in air-insulated substations is important for the electrical equipment statue analysis. The equipment fault detection method based on acoustic signal has been widely studied due to its simplicity and ease of application. In this paper, a novel acoustic equipment fault localization method based on directional of arrival (DOA) estimation is proposed. The main idea of design is to relate the position of the fault source to the established steered response power (SRP) map. Because the closer the position is to the fault source, the higher the SRP amplitude. Thus, by performing peak search of the spectrum is performed to get the directional angle of the fault source acoustic signal. Afterwards, the simulated fault source is moved to another place to repeat the estimation method. The double exponential model for acoustic signal is used for simulation tests and actual estimation system is established for field tests. Results of simulation tests and field tests have shown that the proposed method could detect fault source with high accuracy. The localization error is around 5% of the field of view under actual noise environment, which has verified the feasibility of the proposed method.

引用

页码：1388 / 1392

页数：5

共 13 条

[1] Application Possibilities of Artificial Neural Networks for Recognizing Partial Discharges Measured by the Acoustic Emission Method [J].

Boczar, T. ;

Borucki, S. ;

Cichon, A. ;

Zmarzly, D. .

IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION, 2009, 16 (01) :214-223

[2]

Ching-Chau Su, 2008, Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, CMD 2008, P517, DOI 10.1109/CMD.2008.4580339

[3]

E. M. Test, 2016, DAT DITO ESD SIM

[4]

Hsieh JC, 2012, PROCEEDINGS OF 2012 IEEE INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS (IEEE CMD 2012), P157, DOI 10.1109/CMD.2012.6416399

[5] Artificial recognition system for defective types of transformers by acoustic emission [J].

Kuo, Cheng-Chien .

EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (07) :10304-10311

[6] PD detection and localisation by acoustic measurements in an oil-filled transformer [J].

Lu, Y ;

Tan, X ;

Hu, X .

IEE PROCEEDINGS-SCIENCE MEASUREMENT AND TECHNOLOGY, 2000, 147 (02) :81-85

[7]

Lukianchikov AD, 2017, IEEE NW RUSS YOUNG, P1177, DOI 10.1109/EIConRus.2017.7910768

[8] Study of Improved RSS Algorithm for Ultrasonic Array Applied to PD Location in Transformer [J].

Luo Yongfen ;

Du Fei ;

Pan Wei ;

Gao Jiaping .

IEEE SENSORS JOURNAL, 2015, 15 (09) :4986-4991

[9]

Ma A, 2016, INT CONF CONDIT MON, P733, DOI 10.1109/CMD.2016.7757928

[10]

Najafi SAM, 2016, 2016 IEEE ELECTRICAL INSULATION CONFERENCE (EIC), P523, DOI 10.1109/EIC.2016.7548652

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