Variational mode decomposition-based power system disturbance assessment to enhance WA situational awareness and post-mortem analysis

2017 ◽  
Vol 11 (13) ◽  
pp. 3287-3298 ◽  
Author(s):  
Manas Kumar Jena ◽  
Subhransu Ranjan Samantaray ◽  
Bijaya Ketan Panigrahi
Keyword(s):  
Power System ◽  
Situational Awareness ◽  
Post Mortem ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

scholarly journals Harmonic Detection for Power Grids Using Adaptive Variational Mode Decomposition

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 232 ◽  
Author(s):  
Guowei Cai ◽  
Lixin Wang ◽  
Deyou Yang ◽  
Zhenglong Sun ◽  
Bo Wang
Keyword(s):  
Power System ◽  
Large Scale ◽  
Simulation Analysis ◽  
Harmonic Component ◽  
Power Grids ◽  
Single Frequency ◽  
Variational Model ◽  
Variational Mode Decomposition ◽  
Harmonic Detection ◽  
Mode Decomposition

The harmonic pollution problem in power grids has become increasingly prominent with the large-scale application of power electronic equipment, nonlinear loads, and renewable energy. This study proposes a method based on adaptive variational mode decomposition (AVMD) and Hilbert transform (HT) that is applicable to harmonic detection in power system. The AVMD method constructs and solves the constrained variational model. Then, a single-frequency harmonic component with stable features can be obtained. The proposed method can effectively avoid the recursive process in empirical mode decomposition (EMD). In this study, the variational mode decomposition algorithm is used to obtain the periodic harmonic components concurrently. Subsequently, the characteristic parameters of each harmonic component are extracted via HT. Simulation analysis and measured data verify the validity and feasibility of the proposed algorithm. Compared with the detection results obtained using the EMD algorithm, the proposed method is proven to exhibit stronger applicability to harmonic detection in power system.

scholarly journals Dynamics Analysis Using Koopman Mode Decomposition of a Microgrid Including Virtual Synchronous Generator-Based Inverters

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4581
Author(s):  
Yuko Hirase ◽  
Yuki Ohara ◽  
Naoya Matsuura ◽  
Takeaki Yamazaki
Keyword(s):  
Power System ◽  
System Dynamics ◽  
Dimensional Space ◽  
Supply And Demand ◽  
Transient State ◽  
Synchronous Generator ◽  
Response Analysis ◽  
Power System Dynamics ◽  
Mode Decomposition ◽  
Virtual Synchronous Generator

In the field of microgrids (MGs), steady-state power imbalances and frequency/voltage fluctuations in the transient state have been gaining prominence owing to the advancing distributed energy resources (DERs) connected to MGs via grid-connected inverters. Because a stable, safe power supply and demand must be maintained, accurate analyses of power system dynamics are crucial. However, the natural frequency components present in the dynamics make analyses complex. The nonlinearity and confidentiality of grid-connected inverters also hinder controllability. The MG considered in this study consisted of a synchronous generator (the main power source) and multiple grid-connected inverters with storage batteries and virtual synchronous generator (VSG) control. Although smart inverter controls such as VSG contribute to system stabilization, they induce system nonlinearity. Therefore, Koopman mode decomposition (KMD) was utilized in this study for consideration as a future method of data-driven analysis of the measured frequencies and voltages, and a frequency response analysis of the power system dynamics was performed. The Koopman operator is a linear operator on an infinite dimensional space, whereas the original dynamics is a nonlinear map on a finite state space. In other words, the proposed method can precisely analyze all the dynamics of the power system, which involve the complex nonlinearities caused by VSGs.

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

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