硕士研究生耿闻昊作《交流电弧故障检测技术研究》学术报告

2022-01-13 13:21 680

一、报告人简介:blob.png

        耿闻昊电气学院电器与电子可靠性研究所20级硕士研究生,导师为周学副教授,研究方向为低压交流串联电弧故障检测技术研究


二、报告内容简介:

       本次会议参会人员有:周雨馨、董淼鑫、谢春峰、张月、吴纪昌、赵超凡等。

       会议内容主要讲述关于交流电弧故障检测技术研究的课题背景、研究现状、目前课题进展,包括实验平台的搭建、实验结果及分析、研究方法和取得的成果。


三、讨论内容:

       会议中讨论了火光传感器的不稳定性会导致示波器触发不稳定,火光传感器易受到自然光的影响。机器学习需要自己寻找特征值,对特征量的筛选应该量化,高频线圈的寄生参数不好量化和定制,而且受到电磁干扰影响。

       会议中提出了制作暗箱罩住电弧发生器,防止太阳光等对传感器的影响,可以考虑将机器学习方法换成深度学习再试试,选择合适的原始数据处理手段,高频线圈可以换成其它形状,考虑更好的形式。


四、需要完善、改进的地方:

       降低误判率,提高算法在单片机上运行的可能性。


五、会议现场:

blob.png

六、国内外相关技术前沿:

         产品:eaton AFDD+断路器

                   TIAXI AFDD&AFCI

        参考文献:

[1]      Public Security Fire Department.  (2020,Feb. 06). In 2019, 233,000 fires were reported nationwide [Online].Available: https://www.119.gov.cn/article/3xBeEJjR54K

[2]      N. L. Georgijevic, M. V. Jankovic, S. Srdic and Z. Radakovic, "The Detection of Series Arc Fault in Photovoltaic Systems Based on the Arc Current Entropy,"  IEEE Trans. Power Elect., vol. 31, no. 8, pp. 5917-5930, Aug. 2016.

[3]      General Requirements for Arc Fault Detection Devices,” IEC Standard 62606, 2013

[4]      J. L. Guardado, S. G. Maximov, E. Melgoza, J. L. Naredo and P. Moreno, "An improved arc model before current zero based on the combined Mayr and Cassie arc models," IEEE Trans. Power Del., vol. 20, no. 1, pp. 138-142, Jan. 2005.

[5]      G. Parise, L. Martirano and M. Laurini, "Simplified arc-fault model: The reduction factor of the arc current," 2012 IEEE Industry Applications Society Annual Meeting, Las Vegas, NV, USA, 2012, pp. 1-6.

[6]      M. Rong et al., "3-D MHD Modeling of Internal Fault Arc in a Closed Container,"  IEEE Trans. Power Del., vol. 32, no. 3, pp. 1220-1227, June 2017.

[7]      M. Iwata, S. Tanaka, T. Ohtaka, T. Amakawa, K. Anantavanich and G. J. Pietsch, "CFD Calculation of Pressure Rise Due to Internal AC and DC Arcing in a Closed Container," IEEE Trans. Power Del., vol. 26, no. 3, pp. 1700-1709, July 2011.

[8]      H. Wu, X. Li, D. Stade and H. Schau, "Arc fault model for low-voltage AC systems,"  IEEE Trans. Power Del., vol. 20, no. 2, pp. 1204-1205, April 2005.

[9]      T. S. Sidhu, G. Singh, and M. S. Sachdev, “Microprocessor based instru-ment for detecting and locating electric arcs,” IEEE Trans. Power Del.,vol. 13, no. 4, pp. 1079–1085, Oct. 1998.

[10]  J. C. Kim, D. O. Neacşu, R. Ball and B. Lehman, "Clearing Series AC Arc Faults and Avoiding False Alarms Using Only Voltage Waveforms," IEEE Trans. Power Del., vol. 35, no. 2, pp. 946-956, April 2020.

[11]  Y .-H. Lin, C.-W. Liu, and C.-S. Chen, “A new PMU-based fault detec-tion/location technique for transmission lines with consideration of arcing fault discrimination-part I: Theory and algorithms,” IEEE Trans. Power Del., vol. 19, no. 4, pp. 1587–1593, Oct. 2004.

[12]  O. N. Gerek, D. G. Ece, and A. Barkana, “Covariance analysis of voltage waveform signature for power-quality event classification,” IEEE Trans. Power Del., vol. 21, no. 4, pp. 2022–2031, Oct. 2006.

[13]  I. M. Karmacharya and R. Gokaraju, “Fault location in ungrounded photo- voltaic system using wavelets and ANN,” IEEE Trans. Power Del., vol. 33, no. 2, pp. 549–559, Apr. 2018.

[14]  T. M. Lai, L. A. Snider, E. Lo, and D. Sutanto, “High-impedance faultdetection using discrete wavelet transform and frequency range and RMSconversion,” IEEE Trans. Power Del., vol. 20, no. 1, pp. 397–407, Jan.2005.

[15]  K. Koziy, B. Gou, and J. Aslakson, “A low-cost power-quality meter with series arc-fault detection capability for smart grid,” IEEE Trans. Power Del., vol. 28, no. 3, pp. 1584–1591, Jul. 2013.

[16]  C. H. Kim and R. K. Aggarwal, "Closure on "A novel fault detection technique of high impedance arcing faults in transmission lines using the wavelet transform"," IEEE Trans. Power Del., vol. 17, no. 4, pp. 1596-1597, Oct. 2003.

[17]  P. Qi, S. Jovanovic, J. Lezama and P. Schweitzer,"Discrete wavelet transform optimal parameters estimation for arc fault detection in low-voltage residential power networks, "  Electric Power Systems Research, vol. 143, no. 14,  pp. 130-139, Feb. 2017.

[18]  G. Artale, A. Cataliotti, V. Cosentino, D. Di Cara, S. Nuccio and G. Tine, "Arc Fault Detection Method Based on CZT Low-Frequency Harmonic Current Analysis," IEEE Trans. Inst. Meas., vol. 66, no. 5, pp. 888-896, May 2017.

[19]  Y. Wang, F. Zhang, X. Zhang and S. Zhang, "Series AC Arc Fault Detection Method Based on Hybrid Time and Frequency Analysis and Fully Connected Neural Network," in IEEE Trans. Ind. Info., vol. 15, no. 12, pp. 6210-6219, Dec. 2019.

[20]  L. Wang, H. C. Qiu, P. Yang and L. Mu, "Arc Fault Detection Algorithm Based on Variational Mode Decomposition and Improved Multi-Scale Fuzzy Entropy," Energies, Vol 14, no. 14, pp. 4137-4137, July 2021.

[21]  Y. Wang, F. Zhang and S. Zhang, "A New Methodology for Identifying Arc Fault by Sparse Representation and Neural Network," IEEE Trans. Inst. Meas, Vol. 677, no. 11, pp. 2526-2537, Jan. 2018.

[22]  X. Han, D. Li, L. Huang, H. Huang and Q. Lu, "Series arc fault detection method based on category recognition and artificial neural network," Elec., Vol. 9, no. 9, pp. 1367, Aug. 2020.

[23]  J. Jiang et al., "Series Arc Detection and Complex Load Recognition Based on Principal Component Analysis and Support Vector Machine," IEEE Access, vol. 7, pp. 47221-47229, 2019.

[24]  G. Bao, X. Gao, R. Jiang and K. Huang, "A Novel Differential High-Frequency Current Transformer Sensor for Series Arc Fault Detection, " Sensors (Basel, Switzerland). vol. 19, no. 17, pp. 3649, 2019.

[25]  Chu R , Schweitzer P , Zhang R . Series AC Arc Fault Detection Method Based on High-Frequency Coupling Sensor and Convolution Neural Network[J]. Sensors, 2020, 20(17):4910.

[26]  Su Jingjing, Xu Zhihong. Diagnosis method of multi-variable criterion based on EMD and PNN for arc fault diagnosis [J]. Power automation equipment, 2019,39 (04): 106-113

[27]  Yin Haonan. Feature Analysis and Patter Recognition of Low-voltage Arc Fault [D]. Zhejiang University, 2017

[28]  T. Zhang, R. Zhang, H. Wang, R. Tu and K. Yang, "Series AC Arc Fault Diagnosis Based on Data Enhancement and Adaptive Asymmetric Convolutional Neural Network," IEEE Sensors Journal, vol. 21, no. 18, pp. 20665-20673, 15 Sept.15, 2021.