Accurate and Fast Feature Extraction Method of Power Quality Disturbances Based on Modified S-Transform of Optimal Bohman Window
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摘要: 针对传统S变换存在时频分辨率低且计算量大的问题，该文提出一种基于最优Bohman窗的改进S变换。该方法通过直接控制窗长获得最优时频分辨率，同时只针对主要频率点进行时频分析，实现对各类扰动信号特征的精确快速提取。首先根据所提评价标准确定最优长度参数；其次将采样信号进行快速傅里叶变换得到FFT频谱，再通过基于极大值包络的动态测度快速算法确定主要频率点；然后根据主要频率点所处频段选择对应最优长度参数进行计算处理；最后根据模时频矩阵计算时频幅值向量完成时频特征提取。仿真分析和实验结果表明，所提方法相较于传统S变换具有更高的时频分辨率和更短的计算时间，适用于电能质量扰动信号特征的精确快速提取。Abstract: Focusing on the problems of low time-frequency resolution and large amount of calculation in the traditional S-Transform, a modified S-Transform based on the optimal Bohman window is proposed. In order to extract accurately and quickly the characteristics of all kinds of disturbance signals, this method obtains the optimal time-frequency resolution by controlling directly the window length and carries out time-frequency analysis only for the main frequency points. Firstly, the optimal length parameter is determined according to the proposed evaluation criteria. Secondly, the sampled signal spectrum is obtained through fast Fourier transform, and then the main frequency points are determined by the dynamic measurement fast algorithm based on the maximum envelope; Then the corresponding optimal length parameter is selected according to the frequency band of the main frequency point for calculation and processing; Finally, the time-frequency feature extraction is completed by calculating the time-frequency amplitude vector based on modulus time-frequency matrix. Simulation analysis and experimental results show that the proposed method has higher time-frequency resolution and shorter calculation time than the traditional S-Transform, and is suitable for the accurate and fast extraction of power quality disturbance signal features.
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表 1 计算量和时间复杂度对比
算法 计算量 时间复杂度 复数加法 复数乘法 SMST $\dfrac{N}{2}(N{\log _2}N)$ $\dfrac{N}{2}(N + \dfrac{N}{2}{\log _2}N)$ $ O({N^2}{\log _{\text{2}}}N) $ OST $\dfrac{N}{2}(N{\log _2}N)$ $\dfrac{N}{2}(N + \dfrac{N}{2}{\log _2}N)$ $ O({N^2}{\log _{\text{2}}}N) $ FBST $ m(N{\log _{\text{2}}}N) $ $\dfrac{m}{2}(N + \dfrac{N}{2}{\log _{\text{2} } }N)$ $ O(N{\log _{\text{2}}}N) $ 
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表 3 谐波与暂态振荡信号相对误差对比
扰动 幅值相对误差(×10–5) 频率相对误差(Hz) SMST OST FBST SMST OST FBST 3次谐波 0 0 0 0 0 0 5次谐波 0 0 0 0 0 0 7次谐波 0 0 0 0 0 0 9次谐波 1 0 0 0 0 0 11次谐波 12 0 0 0 0 0 13次谐波 33 0 0 0 0 0 15次谐波 150 1 0 3.3 0 0 17次谐波 195 1 0 6.7 0 0 19次谐波 393 3 0 – 0 0 21次谐波 337 4 0 3.3 0 0 23次谐波 1132 16 0 – 0 0 25次谐波 871 16 0 26.7 0 0 27次谐波 1537 34 0 – 0 0 29次谐波 853 21 0 – 3.3 0 暂态振荡 98 120 101 0 0 0
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表 5 实测信号相对幅值误差(10–5)
扰动 SMST OST FBST C0暂升 6270 2850 1230 C1暂降 5020 2440 1000 C2中断 12590 6025 2529 C3闪变 3710 1970 1300 C4谐波 8412 153 100 C5暂态振荡 481 543 500 C6暂升+谐波 7400 4170 1790 C7暂降+谐波 8950 7100 4130 C8中断+谐波 22570 17805 10464
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[1] 汪颖, 罗代军, 肖先勇, 等. 超高次谐波问题及其研究现状与趋势[J]. 电网技术, 2018, 42(2): 353–365. doi: 10.13335/j.1000-3673.pst.2017.2508WANG Ying, LUO Daijun, XIAO Xianyong, et al. Review and development tendency of research on 2~150 kHz supraharmonics[J]. Power System Technology, 2018, 42(2): 353–365. doi: 10.13335/j.1000-3673.pst.2017.2508 [2] 尹柏强, 陈奇彬, 李兵, 等. 基于改进Kaiser窗快速S变换和LightGBM的电能质量扰动识别与分类新方法[J]. 中国电机工程学报, 2021, 41(24): 8372–8383. doi: 10.13334/j.0258-8013.pcsee.210743YIN Baiqiang, CHEN Qibin, LI Bing, et al. A new method for identification and classification of power quality disturbance based on modified kaiser window fast S-transform and LightGBM[J]. Proceedings of the CSEE, 2021, 41(24): 8372–8383. doi: 10.13334/j.0258-8013.pcsee.210743 [3] 汪飞, 全晓庆, 任林涛. 电能质量扰动检测与识别方法研究综述[J]. 中国电机工程学报, 2021, 41(12): 4104–4120. doi: 10.13334/j.0258-8013.pcsee.201261WANG Fei, QUAN Xiaoqing, and REN Lintao. Review of power quality disturbance detection and identification methods[J]. Proceedings of the CSEE, 2021, 41(12): 4104–4120. doi: 10.13334/j.0258-8013.pcsee.201261 [4] SINGH U and SINGH S N. Application of fractional Fourier transform for classification of power quality disturbances[J]. IET Science, Measurement & Technology, 2017, 11(1): 67–76. doi: 10.1049/iet-smt.2016.0194 [5] 杨超, 张淮清, 王耀, 等. 计及全泄漏影响的多点插值离散傅里叶变换校正方法[J]. 电工技术学报, 2020, 35(16): 3385–3395. doi: 10.19595/j.cnki.1000-6753.tces.190883YANG Chao, ZHANG Huaiqing, WANG Yao, et al. Multipoint interpolated discrete Fourier transform correction method considering total leakage effect[J]. Transactions of China Electrotechnical Society, 2020, 35(16): 3385–3395. doi: 10.19595/j.cnki.1000-6753.tces.190883 [6] SATPATHI K, YEAP Y M, UKIL A, et al. Short-time Fourier transform based transient analysis of VSC interfaced point-to-point DC System[J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 4080–4091. doi: 10.1109/TIE.2017.2758745 [7] 朱茂桃, 吴新佳, 郑国峰, 等. 基于短时傅里叶变换的汽车零部件耐久性载荷信号编辑方法[J]. 机械工程学报, 2018, 55(4): 126–134. doi: 10.3901/JME.2019.04.126ZHU Maotao, WU Xinjia, ZHENG Guofeng, et al. Load signal edition method based on the short-time Fourier transform to durability test of vehicle component[J]. Journal of Mechanical Engineering, 2018, 55(4): 126–134. doi: 10.3901/JME.2019.04.126 [8] 陈正颖, 王黎明, 怡勇. 基于短时傅里叶变换的直流电晕无线电干扰激发电流计算[J]. 高电压技术, 2019, 45(6): 1866–1872. doi: 10.13336/j.1003-6520.hve.20190604024CHEN Zhengying, WANG Liming, and YI Yong. Computation of radio interference excitation current of DC corona based on short-time fourier transform[J]. High Voltage Engineering, 2019, 45(6): 1866–1872. doi: 10.13336/j.1003-6520.hve.20190604024 [9] 代荡荡, 王先培, 龙嘉川, 等. 基于改进Protrugram和小波变换的超高频局部放电信号去噪方法[J]. 高电压技术, 2018, 44(11): 3577–3586. doi: 10.13336/j.1003-6520.hve.20181031017DAI Dangdang, WANG Xianpei, LONG Jiachuan, et al. Denoising method of ultra-high frequency partial discharge signal based on improved protrugram and wavelet transform[J]. High Voltage Engineering, 2018, 44(11): 3577–3586. doi: 10.13336/j.1003-6520.hve.20181031017 [10] THIRUMALA K, PAL S, JAIN T, et al. A classification method for multiple power quality disturbances using EWT based adaptive filtering and multiclass SVM[J]. Neurocomputing, 2019, 334: 265–274. doi: 10.1016/j.neucom.2019.01.038 [11] 吴建章, 梅飞, 郑建勇, 等. 基于改进经验小波变换和XGBoost的电能质量复合扰动分类[J]. 电工技术学报, 2022, 37(1): 232–243,253. doi: 10.19595/j.cnki.1000-6753.tces.201363WU Jianzhang, MEI Fei, ZHENG Jianyong, et al. Recognition of multiple power quality disturbances based on modified empirical wavelet transform and XGBoost[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 232–243,253. doi: 10.19595/j.cnki.1000-6753.tces.201363 [12] 尹柏强, 王署东, 何怡刚, 等. 基于快速S变换时频空间模型的电磁干扰复杂度评估方法[J]. 电子与信息学报, 2019, 41(1): 195–201. doi: 10.11999/JEIT180256YIN Baiqiang, WANG Shudong, HE Yigang, et al. Electromagnetic environment complex evaluation algorithm based on fast s-transform and time-frequency space model[J]. Journal of Electronics &Information Technology, 2019, 41(1): 195–201. doi: 10.11999/JEIT180256 [13] 殷浩然, 苗世洪, 郭舒毓, 等. 基于S变换相关度和深度学习的配电网单相接地故障选线新方法[J]. 电力自动化设备, 2021, 41(7): 88–96. doi: 10.16081/j.epae.202105028YIN Haoran, MIAO Shihong, GUO Shuyu, et al. Novel method for single-phase grounding fault line selection in distribution network based on S-Transform correlation and deep learning[J]. Electric Power Automation Equipment, 2021, 41(7): 88–96. doi: 10.16081/j.epae.202105028 [14] 刘宝稳, 汤容川, 马钲洲, 等. 基于S变换D-SVM AlexNet模型的GIS机械故障诊断与试验分析[J]. 高电压技术, 2021, 47(7): 2526–2535. doi: 10.13336/j.1003-6520.hve.20200224LIU Baowen, TANG Rongchuan, MA Zhengzhou, et al. GIS mechanical fault diagnosis and test analysis based on S-Transform D-SVM AlexNet model[J]. High Voltage Engineering, 2021, 47(7): 2526–2535. doi: 10.13336/j.1003-6520.hve.20200224 [15] 尹柏强, 何怡刚, 朱彦卿. 一种广义S变换及模糊SOM网络的电能质量多扰动检测和识别方法[J]. 中国电机工程学报, 2015, 35(4): 866–872. doi: 10.13334/j.0258-8013.pcsee.2015.04.013YIN Baiqiang, HE Yigang, and ZHU Yanqing. Detection and classification of power quality multi-disturbances based on generalized S-Transform and fuzzy SOM neural network[J]. Proceedings of the CSEE, 2015, 35(4): 866–872. doi: 10.13334/j.0258-8013.pcsee.2015.04.013 [16] 徐艳春, 高永康, 李振兴, 等. 基于VMD初始化S变换的混合动力系统电能质量扰动检测与分类[J]. 中国电机工程学报, 2019, 39(16): 4786–4798. doi: 10.13334/j.0258-8013.pcsee.181861XU Yanchun, GAO Yongkang, LI Zhenxing, et al. Power quality disturbance detection and classification of hybrid power system based on VMD initialization S-transform[J]. Proceedings of the CSEE, 2019, 39(16): 4786–4798. doi: 10.13334/j.0258-8013.pcsee.181861 [17] 王仁明, 汪宏阳, 张赟宁, 等. 基于分段改进S变换和随机森林的复合电能质量扰动识别方法[J]. 电力系统保护与控制, 2020, 48(7): 19–28. doi: 10.19783/j.cnki.pspc.190569WANG Renming, WANG Hongyang, ZHANG Yunning, et al. Composite power quality disturbance recognition based on segmented modified S-Transform and random forest[J]. Power System Protection and Control, 2020, 48(7): 19–28. doi: 10.19783/j.cnki.pspc.190569 [18] TANG Qiu, QIU Wei, and ZHOU Yicong. Classification of complex power quality disturbances using optimized S-transform and kernel SVM[J]. IEEE Transactions on Industrial Electronics, 2020, 67(11): 9715–9723. doi: 10.1109/TIE.2019.2952823 [19] LIANG Chengbin, TENG Zhaosheng, LI Jianmin, et al. A Kaiser window-based S-Transform for time-frequency analysis of power quality signals[J]. IEEE Transactions on Industrial Informatics, 2022, 18(2): 965–975. doi: 10.1109/TII.2021.3083240 [20] 易吉良, 周曼, 李中启, 等. 采用不完全S变换的复杂谐波参数估计[J]. 电工技术学报, 2018, 33(S1): 112–120. doi: 10.19595/j.cnki.1000-6753.tces.180554YI Jiliang, ZHOU Man, LI Zhongqi, et al. Complex harmonic parameters estimation using incomplete S transform[J]. Transactions of China Electrotechnical Society, 2018, 33(S1): 112–120. doi: 10.19595/j.cnki.1000-6753.tces.180554 [21] LI Jianmin, TENG Zhaosheng, TANG Qiu, et al. Detection and classification of power quality disturbances using double resolution S-transform and DAG-SVMs[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(10): 2302–2312. doi: 10.1109/TIM.2016.2578518 [22] 易吉良, 彭建春, 谭会生. 采用不完全S变换的电能质量扰动检测方法[J]. 高电压技术, 2009, 35(10): 2562–2567. doi: 10.13336/j.1003-6520.hve.2009.10.024YI Jiliang, PENG Jianchun, and TAN Huisheng. Detection method of power quality disturbances using incomplete S-Transform[J]. High Voltage Engineering, 2009, 35(10): 2562–2567. doi: 10.13336/j.1003-6520.hve.2009.10.024 [23] 刘应梅, 白晓民, 张红斌, 等. 基于动态测度的电能质量扰动检测[J]. 中国电机工程学报, 2003, 23(10): 57–62. doi: 10.3321/j.issn:0258-8013.2003.10.012LIU Yingmei, BAI Xiaomin, ZHANG Hongbin, et al. The detection of power quality disturbance based on dynamics[J]. Proceedings of the CSEE, 2003, 23(10): 57–62. doi: 10.3321/j.issn:0258-8013.2003.10.012 -
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