A Combined De-Noising Method for Power Quality Disturbances Events
Xiao Xiangui1, Li Kaicheng1, Cai Delong2, Wang Menghao1, Wang Wei1
1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan 430074 China; 2. State Key Laboratory of Power Grid Environmental Protection Wuhan Branch of China Electric Power Research Institute Co. Ltd Wuhan 430074 China
Abstract：The traditional wavelet threshold de-noising algorithm has some disadvantages, such as easy to discard the real signal and poor de-noising effect. This paper presented a new joint de-noising algorithm for power quality disturbance events. Firstly, the disturbance type of signal is preliminarily determined by the number of times that the fading factor of strong tracking Kalman filter is greater than 1, and then different de-noising methods are adopted for different disturbance types. For sinusoidal signal and harmonic signal only containing noise, sparse decomposition and fast Fourier transform (FFT) are used to de-noising twice; fading factor is used to accurately indicate the beginning and ending time of disturbance for sag and rise signal, and sparse decomposition and FFT de-noising are used for each segment of signal; different processing methods are used for signal containing transient pulse and transient oscillation. Firstly, the steady-state component and transient component are obtained by sparse decomposition. The de-noising method of steady-state component is the same as that of sinusoidal signal. The impulse signal and oscillation signal of transient component are processed by retaining the actual value and variational mode decomposition (VMD) respectively. A large number of simulation results show that under different SNR conditions, the proposed algorithm can effectively suppress the noise of various disturbance signals, significantly improve the signal-to-noise ratio, and the effect is better than the wavelet threshold de-noising algorithm.
肖贤贵, 李开成, 蔡得龙, 王梦昊, 王伟. 一种电能质量扰动信号的联合去噪算法[J]. 电工技术学报, 2021, 36(21): 4418-4428.
Xiao Xiangui, Li Kaicheng, Cai Delong, Wang Menghao, Wang Wei. A Combined De-Noising Method for Power Quality Disturbances Events. Transactions of China Electrotechnical Society, 2021, 36(21): 4418-4428.
[1] 王维博, 董蕊莹, 曾文入, 等. 基于改进阈值和阈值函数的电能质量谐波去噪方法[J]. 电工技术学报, 2019, 34(2): 409-418. Wang Weibo, Dong Ruiying, Zeng Wenru, et al.A wavelet de-noising method for power quality based on an improved threshold and threshold function[J]. Transactions of China Electrotechnical Society, 2019, 34(2): 409-418. [2] Srivastava M, Anderson C L, Freed J H.A new wavelet denoising method for selecting decomposition levels and noise thresholds[J]. IEEE Access, 2016(4): 3862-3877. [3] Shukla S, Mishra S, Singh B.Power quality event classification under noisy conditions using EMD-based de-noising techniques[J]. IEEE Transactions on Industrial Informatics, 2014, 10(2): 1044-1054. [4] 刘宇舜, 周文俊, 李鹏飞, 等. 基于广义S变换模时频矩阵的局部放电特高频信号去噪方法[J]. 电工技术学报, 2017, 32(9): 211-220. Liu Yushun, Zhou Wenjun, Li Pengfei, et al.Partial discharge ultrahigh frequency signal denoising method based on generalized S-transform modula time-frequency matrix[J]. Transactions of China Electrotechnical Society, 2017, 32(9): 211-220. [5] 胡卫红, 舒泓, 栾宇光. 基于奇异值分解的电能量信号去噪[J]. 电力系统保护与控制, 2010, 38(2): 30-33. Hu Weihong, Shu Hong, Luan Yuguang.Power quality signals' de-noising method based on singular value decomposition (SVD)[J]. Power System Protection and Control, 2010, 38(2): 30-33. [6] 刘畅. 电力信号去噪与微弱信号检测的研究[D]. 武汉: 华中科技大学, 2019. [7] 张全明, 刘会金, 兰泉妮. 基于小波混合阈值方法的电能质量信号去噪[J]. 电力自动化设备, 2008, 28(8): 28-35. Zhang quanming, Liu Jinhui, Lan Quanni. Power quality signal denoising based on wavelet hybrid threshold method[J]. Electric Power Automation Equipment, 2008, 28(8): 28-35. [8] 李涛, 张宇, 何怡刚. 基于MAP估计双树复小波的电能质量扰动信号去噪方法[J]. 计算技术与自动化, 2012, 31(1): 33-38. Li Tao, Zhang Yu, He Yigang.Dual-tree disturbance signal based on MAP estimation[J]. Computing Technology and Automation, 2012, 31(1): 33-38. [9] 张明, 李开成, 胡益胜. 基于Bayes估计的双小波维纳滤波电能质量信号去噪算法[J]. 电力系统保护与控制, 2011, 39(4): 52-57. Zhang Ming, Li Kaicheng, Hu Yisheng.Power quality signals denoising via double-wavelet Wiener filtering based on Bayes estimation[J]. Power System Protection and Control, 2011, 39(4): 52-57. [10] 范小龙, 谢维成, 蒋文波, 等. 一种平稳小波变换改进阈值函数的电能质量扰动信号去噪方法[J]. 电工技术学报, 2016, 31(14): 219-226. Fan Xiaolong, Xie Weicheng, Jiang Wenbo, et al.An improved threshold function method for power quality disturbance signal de-noising based on stationary wavelet transform[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 219-226. [11] Manikandan M S, Samantaray S R, Kwmwa I.Detection and classification of power quality disturbance using sparse signal decomposition on hybrid dictionaries[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 60(1): 27-38. [12] 何顺帆, 李开成, 蔡得龙, 等. 电能质量扰动数据特征的最优分辨率提取[J]. 中国电机工程学报, 2018, 38(21): 6223-6232. He Shunfan, Li Kaicheng, Cai Delong, et al.Feature extraction of power quality disturbance with optimal resolution[J]. Proceedings of the CSEE, 2018, 38(21): 6223-6232. [13] Shunfan He, Wei Tian, Junming Zhang, et al.A high efficient approach for power disturbance waveform compression in the view of Heisenberg uncertainty[J]. IEEE Transactions on Industrial Informatics, 2019, 15(5): 2580-2591. [14] Cai Delong, Li Kaicheng, He Shunfan, et al.On the application of joint-domain dictionary mapping for multiple power disturbance assessment[J]. Energies, 2018, 11(2): 347. [15] 蔡得龙. 基于稀疏分解的电能质量研究[D]. 武汉: 华中科技大学, 2018. [16] 王文飞, 周雒维, 李绍令, 等. 采用改进CPSO动态搜索时频原子的电能质量扰动信号去噪方法[J]. 电网技术, 2018, 48(12): 4129-4137. Wang Wenfei, Zhou Luowei, Li Shaoling, et al.A time-frequency atoms dynamic search method for power quality disturbance signal de-noising based on improved chaotic particle swarm optimization[J]. Power System Technology, 2018, 48(12): 4129-4137. [17] 陶顺, 唐松浩, 陈聪, 等. 变频调速器电压暂降耐受特性试验及量化方法研究Ⅰ:机理分析与试验方法[J]. 电工技术学报, 2019, 34(6): 1273-1281. Tao Shun, Tang Songhao, Chen Cong, et al.Experimental research on adjustable speed drivers tolerance to voltage sags and quantitative method partⅠ: mechanism analysis and test method[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1273-1281. [18] 唐松浩, 陶顺, 刘颖英, 等. 变频调速器电压暂降耐受特性试验及量化方法研究Ⅱ:试验及量化方法[J]. 电工技术学报, 2019, 34(10): 2207-2215. Tang Songhao, Tao Shun, Liu Yingying, et al.Experimental research on adjustable speed drivers tolerance to voltage sags and quantitative method partⅡ: experiment and quantification method[J]. Transactions of China Electrotechnical Society, 2019, 34(10): 2207-2215. [19] 周凯, 黄永禄, 谢敏, 等. 短时奇异值分解用于局放信号混合噪声抑制[J]. 电工技术学报, 2019, 34(11): 2435-2443. Zhou Kai, Huang Yonglu, Xie Min, et al.Mixed noises suppression of partial discharge signal employing short-time singular value decomposition[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2435-2443. [20] 张嫣, 林涌艺, 邵振国. 电压暂降可观约束下的定位监测点多目标优化配置[J]. 电工技术学报, 2019, 34(11): 2375-2383. Zhang Yan, Lin Yongyi, Shao Zhenguo.Multi-objective optimal allocation of monitors for voltage sag location under observability constraint[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2375-2383. [21] 黄小平, 王岩. 卡尔曼滤波原理及应用—MATLAB仿真[M]. 北京: 电子工业出版社, 2015. [22] 周东华, 席裕庚, 张钟俊. 非线性系统带次优渐消因子的扩展卡尔曼滤波[J]. 控制与决策, 1990, 5(5): 1-6. Zhou Donghua, Xi Yugeng, Zhang Zhongjun.Suboptimal fading extended Kalman filter for nonlinear systems[J]. Control and Decision, 1990, 5(5): 1-6. [23] 陈晓静. 非平稳复合电能质量扰动检测研究[D]. 武汉: 华中科技大学, 2018. [24] 牛胜锁, 王康乐, 梁志瑞. 基于改进强跟踪无迹卡尔曼滤波的电力系统同步相量估计方法[J]. 电网技术, 2019, 43(9): 3218-3225. Niu Shengsuo, Wang Kangle, Liang Zhirui.Synchronous phasor estimation method for power system based on modified strong tracking unscented Kalman filter[J]. Power System Technology, 2019, 43(9): 3218-3225. [25] Dragomiretskiy K, Zosso D.Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544. [26] 龚静. 电能质量信号去噪中小波选取特点的研究[J/OL]. 电测与仪表, 2020, https://kns.snki.net/ kcms/detail/23.1202.TH.20200728.0838.002.html. Gong Jing. Research on the characteristics of wavelet selection in power quality signal de-noising[J/OL]. Electrical Measurement & Instrumentation, 2020, https://kns.snki.net/kcms/detail/23.1202.TH.20200728.0838.002.html
2021, Vol. 36 
