基于混沌理论和GOA-K-means算法的有载分接开关状态特征分析计算方法

doi:10.19595/j.cnki.1000-6753.tces.201174

Abstract
Figure/Table
References
Related Citation (13)

Download: PDF (1249 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract To monitor the mechanical state of on-load tap changer (OLTC) more accurately and effectively, considering the situation that the clustering effect of traditional monitoring methods based on K-means are affected by the selection of their initial clustering centers, this paper proposed an OLTC mechanical state monitoring method based on the combination of grasshopper optimization algorithm (GOA) and K-means. Firstly, on account of the nonlinear and chaotic characteristics of OLTC vibration signals, the embedding dimension and delay time are calculated by the P-G method and mutual information value method, and the phase space of the measured OLTC vibration signals was reconstructed. Secondly, Kolmogorov entropy was applied to judge the chaotic characteristics of the reconstructed vibration signals. Finally, to improve the clustering accuracy, according to the sensitivity of K-means to the initial clustering center, GOA was introduced into the algorithm to optimize its clustering center, and the reconstructed high-dimensional vibration signals were analyzed by an optimized K-means clustering method. The results showed that in the application of OLTC vibration signal identification, the calculation results obtained by the optimized K-means clustering algorithm have certain regularity, which provides a new way for OLTC machines running state monitoring.

Key words： Grasshopper optimization algorithms on-load tap-changer optimized K-means vibration signal

Received: 03 September 2020

PACS:

TM403.4

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Ma Hongzhong
	Yan Yan

Cite this article:

Ma Hongzhong,Yan Yan. Analysis and Calculation Method of On-Load Tap Changers State Characteristics Based on Chaos Theory and Grasshopper Optimization Algorithm-K-means Algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1399-1406.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.201174 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I7/1399

[1] 姜益民. 上海电网有载分接开关运行分析[J]. 上海电力, 2006, 19(4): 386-394.
Jiang Yimin.Analysis of OLTC running in shanghai power grid[J]. Shanghai Electrical Power, 2006, 19(4): 386-394.
[2] Bengtsson C.Status and trends in transformer monitoring[J]. IEEE Transactions on Power Delivery, 1996, 11(3): 1379-1384.
[3] Simas F E F, de Almeida L A L, de C Lima A C. Vibration monitoring of on-load tap changers using a genetic algorithm[C]//IEEE Instrumentation & Measurement Technology Conference, Ottawa, Ont, Canada, 2005: 2288-2293.
[4] 张燕, 方瑞明. 基于油中溶解气体动态网络标志物模型的变压器缺陷预警与辨识[J]. 电工技术学报, 2020, 35(9): 2032-2041.
Zhang Yan, Fang Ruiming.Fault detection and identification of transformer based on dynamical network marker model of dissolved gas in oil[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2032-2041.
[5] Yang Ruilin, Zhang Dandan, Li Zhenbiao, et al.Mechanical fault diagnostics of power transformer on-load tap changers using dynamic time warping[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(9): 3119-3217.
[6] 杨森, 陈莎莎, 李冠, 等. 基于变分模态分解与特征选择的变压器有载分接开关机械故障诊断[J].南方电网技术, 2019, 13(1): 39-47, 59.
Yang Sen, Chen Shasha, Li Guan, et al.Mechanical fault diagnosis of transformer on-load tap-changer based on variational mode decomposition and feature selection[J]. Southern Power Systems Technology, 2019, 13(1): 39-47, 59.
[7] 张静, 张丹丹, 程林, 等. 基于振动信号的有载分接开关故障模拟试验研究[J]. 高压电器, 2018, 54(10): 97-105.
Zhang Jing, Zhang Dandan, Cheng Lin, et al.Experimental research on fault simulation of on-load tap changer based on vibration signals[J]. High Voltage Apparatus, 2018, 54(10): 97-105.
[8] Bengtsson C, Kols H, Martinsson L, et al. Acoustic diagnosis of on tap changers[C]//CIGRE1996 Session, France: CIGRE, 1996: 32-INT-CANADA- 12-1.
[9] Rivas E, Burgos J C, Garcia-Prada J C. Condition Assessment of power OLTC by vibration analysis using wavelet transform[J]. IEEE Transactions on Power Delivery, 2009, 24(2): 687-694.
[10] Li Qingmin, Zhao Tong, Zhang Li, et al.Mechanical fault diagnostics of onload tap changer within power transformers based on hidden Markov model[J]. IEEE Transactions on Power Delivery, 2012, 27(2): 596-601.
[11] 赵彤, 张黎, 李庆民. OLTC振动信号的多维空间相轨图几何特征[J]. 高电压技术, 2007, 33(8): 102-105.
Zhao Tong, Zhang Li, Li Qingming.Feature analysis methodology for phase portrait structure of mechanical vibration signals of on-load tap changers, multidimensional space[J]. High Voltage Engineering, 2007, 33(8): 102-105.
[12] 张知先, 陈伟根, 汤思蕊, 等. 基于互补集总经验模态分解和局部异常因子的有载分接开关状态特征提取及异常状态诊断[J]. 电工技术学报, 2019, 34(21): 4508-4518.
Zhang Xianzhi, Chen Weigeng, Tang Sirui, et al.State feature extraction and anomaly diagnosis of on-load tap-changer based on complementary ensemble empirical mode decomposition and local outlier factor[J]. Transactions of China Electro tech nical Society ,2019,34(21):4508-4518.
[13] 周翔, 王丰华, 傅坚, 等. 基于混沌理论和K-means聚类的有载分接开关机械状态监测[J]. 中国电机工程学报, 2015, 35(6): 1541-1548.
Zhou Xiang, Wang Fenghua, Fu Jian, et al.Mechanical condition monitoring of on-load tap changers based on chaos theory and K-means clustering method[J]. Proceedings of the CSEE, 2015, 35(6): 1541-1548.
[14] Saremi S, Mirjalili S, Lewis A.Grasshopper optimisation algorithm: theory and application[J]. Advances in Engineering Software, 2017, 105: 30-47.
[15] Mirjalili S Z, Mirjalili S, Saremi S, et al.Grasshopper optimization algorithm for multi-objective optimization problems[J]. Applied Intelligence, 2017, 48(8): 805-820.
[16] Mafarja M, Aljarah I, Heidari A A, et al.Evolutionary population dynamics grasshopper algorithm approaches for feature selection problems[J]. Knowledge Based Systems, 2018, 145: 25-45.
[17] 闫旭, 叶春明. 混合蝗虫优化算法求解作业车间调度问题[J]. 计算机工程与应用, 2019, 55(6): 257-264.
Yan Xu, Ye Chunming.Hybrid grasshopper optimization algorithm for job-shop scheduling problem[J]. Computer Engineering and Applications. 2019, 55(6): 257-264.
[18] 程泽新, 李东生, 高杨. 基于蝗虫算法的无人机三维航迹规划[J]. 飞行力学, 2019, 37(2): 46-50.
Chen Zexin, Li Dongsheng, Gao Yang.UAV three-dimensional path planning based on the grasshopper algorithm[J]. Flight Dynamics, 2019, 37(2): 46-50.
[19] 潘峰, 孙红霞. 基于蝗虫算法的图像多阈值分割方法[J]. 电子测量与仪器报, 2019, 33(1): 149-155.
Pan Feng, Sun Hongxia.Algorithm for image segmentation based on grasshopper optimization algorithm[J]. Journal of Electronic Measurement and Instruments, 2019, 33(1):149-155.
[20] Zhang Xin, Miao Qiang, Zhang Heng, et al.A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery[J]. Mechanical Systems and Signal Processing, 2018, 108: 58-72.
[21] 李恩文,王力农,宋斌,方雅琪.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, eta al. Parallel clustering analysis of dissolved gas analysis data based on chaotic sequences[J].Transactions of China Electrotechnical Society, 2019, 34(24): 5104-5114.
[22] 吕金龙, 陆君安, 陈士华. 混沌时间序列分析及其应用[M]. 武汉: 武汉大学出版社, 2001.
[23] Albano A M, Muench J, Schwartz C, et al.Singular value decomposition and the grassberger-procaccia algorithm[J]. Physical Review A, 1988, 38(6): 3017-3026.
[24] 郭伯和, 金宁德, 胡亚范, 等. 油水两相流流型的混沌时间序列分析[J]. 动力学与控制学报, 2004, 2(3): 50-55.
Guo Baihe, Jin Ningde, Hu Yafan, et al.Chaotic time series analysis of oil/water two phase flow patterns[J]. Journal of Dynamics and Control, 2004, 2(3):50-55.
[25] 钱瑾, 王培红, 李琳. 聚类算法在锅炉运行参数基准值分析中的应用[J]. 中国电机工程学报, 2007, 27(23): 71-74.
Qian Jin, Wang Peihong, Li Lin.Application of clustering algorithm in target-value analysis for boiler operating parameter[J]. Proceedings of the CSEE, 2007, 27(23): 71-74.
[26] 杨雨航. 动态粒子群优化K-means的图像分割算法研究[J]. 现代计算机, 2019(8): 63-67.
Yang Yuhang.Image segmentation algorithm based on improved particle swarm optimization and K-means clustering[J]. Modern Computer, 2019(8): 63-67.
[27] Grassberger P, Procaccia I.Estimation of the Kolmogorov entropy from a chaotic signal[J]. Physical Review A, 1983, 28(4): 2591-2593.