Power System Stability Study with Empirical Mode Decomposition

2013 ◽  
Vol 732-733 ◽  
pp. 905-908 ◽  
Author(s):  
Chia Liang Lu ◽  
Pei Hwa Huang
Keyword(s):  
Power System ◽  
Empirical Mode Decomposition ◽  
Time Domain ◽  
Power System Stability ◽  
Direct Analysis ◽  
Stability Study ◽  
System Stability ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition

Low frequency oscillations (LFO) reflect the damping and the stability of a power system and is essentially non-stationary. The LFO is a composite response of various oscillation modes and of which the frequency may be changing with time; thus, direct analysis of such time-domain responses is difficult. The main purpose of this paper is to apply the method of empirical mode decomposition (EMD) to the study of power system stability. First the method of EMD is to expand the time-domain responses under study into multiple intrinsic mode functions (IMFs). Then the 2D time-frequency information inherent in the response under study is obtained using the wavelet transform. The 2D time-frequency graph is further expanded into a 3D time-frequency-energy graph. Information from the 3D time-frequency graph is analyzed to determine those generators that have higher extent of oscillation involvement during the occurrence of LFO in the power system. The results from comparative analysis show that, at specific frequencies from LFOs, higher extent of oscillation involvement will reveal a greater factor of involvement in the frequency domain behavior.

Gird Lagrange Stability Analysis Based on Quasi Periodic Analysis

2013 ◽  
Vol 336-338 ◽  
pp. 570-574
Author(s):  
Li Fang Lu ◽  
Huan Qi ◽  
Xun Cheng Huang
Keyword(s):  
Power System ◽  
Power System Stability ◽  
Ensemble Empirical Mode Decomposition ◽  
Stability Study ◽  
System Stability ◽  
Trajectory Data ◽  
Lagrange Stability ◽  
Mode Decomposition ◽  
Periodic Analysis ◽  
Simulation Results

The Lagrange stability is a new concept in power system stability study. In this paper, an effective method based on quasi-periodic analysis has be presented to analyze the power system Lagrange stability. The Ensemble Empirical Mode Decomposition (EEMD) has be introduced to deal with the trajectory data of power system in the proposed approach. With the EEMD decomposition and linear transformation of the trajectory data, a special constantAcan be accepted. IfAequal to zero, the power system is Lagrange stable, otherwise the power system is not Lagrange stable. The simulation results show the correctness of the method.

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

2021 ◽  
Author(s):  
Chun-Hsiang Tang ◽  
Christina W. Tsai
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Spatial Locality ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Climatic Scenarios

<p>Abstract</p><p>Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.</p><p>In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.</p><p> </p><p>Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought</p>

scholarly journals Testing a differential-algebraic equation solver in long-term voltage stability simulation

2006 ◽  
Vol 2006 ◽  
pp. 1-13
Author(s):  
José E. O. Pessanha ◽  
Alex A. Paz
Keyword(s):  
Power System ◽  
Algebraic Equation ◽  
Time Domain ◽  
Voltage Stability ◽  
Power System Stability ◽  
System Stability ◽  
Variable Order ◽  
Differential Algebraic Equation ◽  
Short Period

This work evaluates the performance of a particular differential-algebraic equation solver, referred to as DASSL, in power system voltage stability computer applications. The solver is tested for a time domain long-term voltage stability scenario, including transient disturbances, using a real power system model. Important insights into the mechanisms of the DASSL solver are obtained through the use of this real model, including control devices relevant to the simulated phenomena. The results indicate that if properly used, the solver can be a powerful numerical tool in time domain assessment of long-term power system stability since it comprises, among several important features, suitable and very efficient variable order and variable step-size numerical techniques. These characteristics are very important when CPU time is a great concern, which is the case when the power system operator needs reliable results in a short period of time. Prior to the present work, this solver has never been applied in power system stability computer analysis in time domain considering slow and fast phenomena.

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

scholarly journals A Highly Effective Data Preprocessing in Side-Channel Attack Using Empirical Mode Decomposition

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
ShuaiWei Zhang ◽  
XiaoYuan Yang ◽  
Lin Chen ◽  
Weidong Zhong
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Data Preprocessing ◽  
Side Channel ◽  
Intrinsic Mode Functions ◽  
Side Channel Attack ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Highly Effective ◽  
Preprocessing Technique

Side-channel attacks on cryptographic chips in embedded systems have been attracting considerable interest from the field of information security in recent years. Many research studies have contributed to improve the side-channel attack efficiency, in which most of the works assume the noise of the encryption signal has a linear stable Gaussian distribution. However, their performances of noise reduction were moderate. Thus, in this paper, we describe a highly effective data-preprocessing technique for noise reduction based on empirical mode decomposition (EMD) and demonstrate its application for a side-channel attack. EMD is a time-frequency analysis method for nonlinear unstable signal processing, which requires no prior knowledge about the cryptographic chip. During the procedure of data preprocessing, the collected traces will be self-adaptably decomposed into sum of several intrinsic mode functions (IMF) based on their own characteristics. And then, meaningful IMF will be reorganized to reduce its noise and increase the efficiency of key recovering through correlation power analysis attack. This technique decreases the total number of traces for key recovering by 17.7%, compared to traditional attack methods, which is verified by attack efficiency analysis of the SM4 block cipher algorithm on the FPGA power consumption analysis platform.

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