Feature Analysis for Transient Overvoltage in Offshore Wind Farm Based on High and Low Frequency Energy Rate Using Multi-scale Mathematical Morphology

被引:0
作者
Gu Y. [1 ]
Tang W. [1 ]
Xin Y. [1 ]
Zhou J. [1 ]
Wu Q. [1 ]
机构
[1] School of Electric Power Engineering, South China University of Technology, Guangzhou
来源
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2021年 / 41卷 / 05期
基金
中国国家自然科学基金;
关键词
High and low frequency energy; Internal transient overvoltage; Multi-scale mathematical morphology; Offshore wind farm;
D O I
10.13334/j.0258-8013.pcsee.191869
中图分类号
学科分类号
摘要
At present, the high-frequency transient overvoltage caused by the frequent operations on electrical equipment or faults in offshore wind farms, is particularly severe. In order to analyze the transient characteristics of internal overvoltage in offshore wind farms, this paper firstly proposed a signal feature extraction method based on mathematical morphology, constructing a new morphological structure operator and utilizing multi-scale mathematical morphological decomposition to extract the high and low frequency information of transient overvoltage. Then two time-domain identification feature indexes were constructed for identifying the type of transient overvoltage in offshore wind farms. Finally, based on the high frequency feature index and the high and low frequency energy rate feature index proposed by this paper, different types of internal transient overvoltage classification could be classified using the support vector machine classifier model. The results show that compared with the traditional Wavelet algorithm, the feature extracted by the proposed mathematical morphology algorithm had a clearer degree of discrimination, which could accurately identify the overvoltage types, laying a foundation for the protection setting and insulation coordination for the electrical equipment in the offshore wind farm substations. © 2021 Chin. Soc. for Elec. Eng.
引用
收藏
页码:1702 / 1712
页数:10
相关论文
共 37 条
[1]  
WANG Jiandong, LI Guojie, QIN Huan, Simulation of switching over-voltages in the collector networks of offshore wind farm, Automation of Electric Power Systems, 34, 2, pp. 104-105, (2010)
[2]  
CORNICK K, THOMPSON T R., Steep-fronted switching voltage transients and their distribution in motor windings: part I system measurements of steep-fronted switching voltage transients, IEE Proceedings-Electric Power Applications, 129, 2, pp. 45-55, (1982)
[3]  
SHIPP D D, DIONISE T J, LORCH V, Et al., Transformer failure due to circuit breaker induced switching transients, IEEE Transactions on Industry Applications, 47, 2, pp. 707-718, (2011)
[4]  
CHEN Xiaoqin, HE Zhengyou, FU Ling, Electric power transient signals classification and recognition method based on wavelet energy spectrum, Power System Technology, 30, 17, pp. 59-69, (2006)
[5]  
LIANG Dong, XU Bingyin, XIE Wei, Et al., A time-domain-reflectometry cable fault location method using wavelet pulses and cross-correlation, Proceedings of the CSEE, 40, 24, pp. 8050-8057, (2020)
[6]  
WU Yu, TANG Qiu, TENG Zhaosheng, Et al., Feature extraction method of power quality disturbance signals based on modified S-transform, Proceedings of the CSEE, 36, 10, pp. 2682-2689, (2016)
[7]  
XU Yanchun, GAO Yongkang, LI Zhenxing, Et al., Power quality disturbance detection and classification of hybrid power system based on VMD initialization S-transform, Proceedings of the CSEE, 39, 16, pp. 4786-4798, (2019)
[8]  
CHEN K J, HU J, HE J L., A framework for automatically extracting overvoltage features based on sparse autoencoder, IEEE Transactions on Smart Grid, 9, 2, pp. 1-1, (2016)
[9]  
CHEN H Y, ASSALA P D S, CAI Y Z, Et al., Intelligent transient overvoltages location in distribution systems using wavelet packet decomposition and general regression neural networks, IEEE Transactions on Industrial Informatics, 12, 5, pp. 1726-1735, (2016)
[10]  
YANG Saizhao, XIANG Wang, ZHANG Junjie, The artificial neural network based fault detection method for the overhead MMC based DC grid, Proceedings of the CSEE, 39, 15, pp. 4416-4430, (2019)