Forced Oscillation Location in Power Systems Using Adaptive Projection Intrinsically Transformed Multiple Empirical Mode Decomposition
Jiang Tao, Liu Bohan, Li Xue, Li Guoqing
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China
Abstract：In recent years, forced oscillation occurs frequently in the power grid, which seriously threatens the stability of the power grid. Fast and accurate forced oscillation source location is very important to mitigate the forced oscillation. However, traditional methods are difficult to accurately extract the forced oscillation component from the multi-channel measurements, which significantly affects the accuracy of forced oscillation source location. To cope with this shortcoming, this paper proposes an adaptive-projection intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD) based forced oscillation source location method. By accurately extracting the forced oscillation component implied in the multichannel measurements, the location accuracy and computational efficiency of the forced oscillation source are effectively improved. Firstly, the intrinsic mode functions (IMFs) of the measurements, which represent different oscillation modes, are decomposed from the multichannel measurements via the proposed APIT-MEMD. Then, the IMF associated with the forced oscillation mode is separated from the decomposed IMFs by using the logarithmic energy entropy. Further, the APIT-MEMD-based dissipation energy flow (DEF) of each generator is calculated using the separated forced oscillation IMF, and the forced oscillation source is located by using the criterions of forced oscillation source. In this method, the proposed APIT-MEMD method can adaptively construct the projection direction vector to estimate the local optimal mean, and the IMF component associated with the forced oscillation mode is extracted accurately and completely. Forced oscillation source location results of the WECC 179-bus test system and power grid field-measurements show that, the proposed APIT-MEMD method can accurately and completely extract the IMF component associated with the forced oscillation mode, which effectively improves the location accuracy and efficiency of forced oscillation source. Compared with EMD, the proposed method extracts 4 IMF components for each measurement, and the oscillation frequencies of IMFs decomposed by each channel are basically same. However, the extracted IMF components for each measurement of EMD method are different. Compared with the MEMD method, the proposed method decomposes fewer IMFs, which avoids excessive decomposition of signals on the basis of effectively extracting forced oscillation mode components. Furthermore, compared with the EMD and MEMD method, the dissipation energy flow calculated by the proposed method has a more obvious downward trend and is easier to locate the forced oscillation source, and the computational efficiency is improved by 84.3% and 62.2% respectively. The following conclusions can be drawn from the simulation analysis: (1) Compared with EMD, the proposed method can extract forced oscillation components of multi-channel wide-area measurements synchronously. (2) Compared with the MEMD method, the proposed method can avoid the excessive decomposition of the multi-channel wide-area measurements and improves the calculation accuracy by accurately estimating the local optimal mean. (3) The forced oscillation source location method based on APIT-MEMD proposed in this paper is completely independent of the detailed model and accurate parameters of the system. The forced oscillation source can be accurately and effectively located only according to the wide-area measurements of the generator. (4) Compared with the traditional DEF method, the proposed method effectively avoids the interference of the redundant information in the wide-area measurement to the forced oscillation source location, and has higher forced oscillation source location accuracy.
姜涛, 刘博涵, 李雪, 李国庆. 基于自适应投影多元经验模态分解的电力系统强迫振荡源定位[J]. 电工技术学报, 2023, 38(13): 3527-3538.
Jiang Tao, Liu Bohan, Li Xue, Li Guoqing. Forced Oscillation Location in Power Systems Using Adaptive Projection Intrinsically Transformed Multiple Empirical Mode Decomposition. Transactions of China Electrotechnical Society, 2023, 38(13): 3527-3538.
[1] 薛安成, 王嘉伟. 基于非光滑分岔的单机水电系统超低频频率振荡机理分析[J]. 电工技术学报, 2020, 35(7): 1489-1497. Xue Ancheng, Wang Jiawei.Mechanism analysis of ultra-low frequency oscillation of single hydropower system based on non-smooth bifurcation[J]. Transactions of China Electrotechnical Society, 2020, 35(7): 1489-1497. [2] 陈剑, 杜文娟, 王海风. 采用深度迁移学习定位含直驱风机次同步振荡源机组的方法[J]. 电工技术学报, 2021, 36(1): 179-190. Chen Jian, Du Wenjuan, Wang Haifeng.A method of locating the power system subsynchronous oscillation source unit with grid-connected PMSG using deep transfer learning[J]. Transactions of China Electrotechnical Society, 2021, 36(1): 179-190. [3] 李雪, 于洋, 姜涛, 等. 基于稀疏增强动态解耦的电力系统振荡模式与模态辨识方法[J]. 电工技术学报, 2021, 36(13): 2832-2843. Li Xue, Yu Yang, Jiang Tao, et al.Sparsity promoting dynamic mode decomposition based dominant modes and mode shapes estimation in bulk power grid[J]. Transactions of China Electrotechnical Society, 2021, 36(13): 2832-2843. [4] 冯双, 陈佳宁, 汤奕, 等. 基于SPWVD图像和深度迁移学习的强迫振荡源定位方法[J]. 电力系统自动化, 2020, 44(17): 78-91. Feng Shuang, Chen Jianing, Tang Yi, et al.Location method of forced oscillation source based on SPWVD image and deep transfer learning[J]. Automation of Electric Power Systems, 2020, 44(17): 78-91. [5] 李阳海, 黄莹, 刘巨, 等. 基于阻尼转矩分析的电力系统低频振荡源定位[J]. 电力系统保护与控制, 2015, 43(14): 84-91. Li Yanghai, Huang Ying, Liu Ju, et al.Power system oscillation source location based on damping torque analysis[J]. Power System Protection and Control, 2015, 43(14): 84-91. [6] Chen Lei, Min Yong, Chen Yiping, et al.Evaluation of generator damping using oscillation energy dissipation and the connection with modal analysis[J]. IEEE Transactions on Power Systems, 2014, 29(3): 1393-1402. [7] Dosiek L, Zhou Ning, Pierre J W, et al.Mode shape estimation algorithms under ambient conditions: a comparative review[J]. IEEE Transactions on Power Systems, 2013, 28(2): 779-787. [8] Myers R B, Trudnowski D J.Effects of forced oscillations on spectral-based mode-shape estimation[C]//2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, Canada, 2013: 1-6. [9] Sarmadi S A N, Venkatasubramanian V. Inter-area resonance in power systems from forced oscillations[J]. IEEE Transactions on Power Systems, 2016, 31(1): 378-386. [10] 董清, 梁晶, 颜湘武, 等. 大规模电网中低频振荡扰动源的定位方法[J]. 中国电机工程学报, 2012, 32(1): 78-83, 15. Dong Qing, Liang Jing, Yan Xiangwu, et al.Locating method of disturbance source of low frequency oscillation in large scale power grid[J]. Proceedings of the CSEE, 2012, 32(1): 78-83, 15. [11] 李文锋, 郭剑波, 李莹, 等. 基于WAMS的电力系统功率振荡分析与振荡源定位(1)割集能量法[J]. 中国电机工程学报, 2013, 33(25): 41-46, 9. Li Wenfeng, Guo Jianbo, Li Ying, et al.Power system oscillation analysis and oscillation source location based on WAMS part 1: method of cutset energy[J]. Proceedings of the CSEE, 2013, 33(25): 41-46, 9. [12] 陈磊, 闵勇, 胡伟. 基于振荡能量的低频振荡分析与振荡源定位 (一)理论基础与能量流计算[J]. 电力系统自动化, 2012, 36(3): 22-27, 86. Chen Lei, Min Yong, Hu Wei.Low frequency oscillation analysis and oscillation source location based on oscillation energy part one mathematical foundation and energy flow computation[J]. Automation of Electric Power Systems, 2012, 36(3): 22-27, 86. [13] Maslennikov S.Dissipating energy flow method for locating the source of sustained oscillations[J]. International Journal of Electrical Power & Energy Systems, 2017, 88: 55-62. [14] 姜涛, 张明宇, 李雪, 等. 基于正交子空间投影的电力系统同调机群辨识[J]. 电工技术学报, 2018, 33(9): 2077-2088. Jiang Tao, Zhang Mingyu, Li Xue, et al.Estimating coherent generators from measurement responses in power systems using orthogonal subspace projection[J]. Transactions of China Electrotechnical Society, 2018, 33(9): 2077-2088. [15] Chen Lei, Sun Ming, Min Yong, et al.Online monitoring of generator damping using dissipation energy flow computed from ambient data[J]. IET Generation, Transmission & Distribution, 2017, 11(18): 4430-4435. [16] Estevez P G, Marchi P, Galarza C, et al.Non-stationary power system forced oscillation analysis using synchrosqueezing transform[J]. IEEE Transactions on Power Systems, 2021, 36(2): 1583-1593. [17] 姜涛, 李孟豪, 李雪, 等. 电力系统强迫振荡源的时频域定位方法[J]. 电力系统自动化, 2021, 45(9): 98-106. Jiang Tao, Li Menghao, Li Xue, et al.Time-frequency domain location method for forced oscillation source in power system[J]. Automation of Electric Power Systems, 2021, 45(9): 98-106. [18] 褚晓杰, 印永华, 高磊, 等. 基于经验模态理论的强迫振荡扰动源定位新方法[J]. 中国电机工程学报, 2014, 34(28): 4906-4912. Chu Xiaojie, Yin Yonghua, Gao Lei, et al.A new forced oscillation disturbance source location method based on empirical mode theory[J]. Proceedings of the CSEE, 2014, 34(28): 4906-4912. [19] 姜涛, 刘博涵, 李雪, 等. 基于多元经验模态分解的电力系统强迫振荡源定位[J]. 中国电机工程学报, 2022, 42(22): 8063-8075. Jiang Tao, Liu Bohan, Li Xue, et al.Forced oscillation location in power systems using multiple empirical mode decomposition[J]. Proceedings of the CSEE, 2022, 42(22): 8063-8075. [20] Min Yong, Chen Lei.A transient energy function for power systems including the induction motor model[J].Science in China Series E: Technological Sciences, 2007, 50(5): 575-584. [21] 张艳军, 殷祥翔, 葛延峰, 等. 基于APIT-MEMD的电力系统低频振荡模式辨识新方法[J]. 电力系统保护与控制, 2020, 48(14): 165-174. Zhang Yanjun, Yin Xiangxiang, Ge Yanfeng, et al.Low frequency oscillation mode estimation in power systems using adaptive-projection intrinsically transformed multivariate empirical mode decom-position[J]. Power System Protection and Control, 2020, 48(14): 165-174. [22] Hemakom A, Goverdovsky V, Looney D, et al.Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications[J]. Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 2016, 374(2065): 20150199. [23] Krishnan P, Yaacob S.Drowsiness detection using band power and log energy entropy features based on EEG signals[J]. International Journal of Innovative Technology and Exploring Engineering, 2019, 8(10): 830-836.
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