Extraction and Analysis of Inter-area Oscillation Using Improved Multi-signal Matrix Pencil Algorithm Based on Data Reduction in Power System

2016 ◽  
Vol 17 (4) ◽  
pp. 435-450 ◽  
Author(s):  
Cheng Liu ◽  
Guowei Cai ◽  
Deyou Yang ◽  
Zhenglong Sun
Keyword(s):  
Power System ◽  
Mode Shape ◽  
Oscillation Mode ◽  
Matrix Pencil ◽  
Identification Algorithm ◽  
Original Algorithm ◽  
The Matrix ◽  
Output Only ◽  
Value Decomposition ◽  
Computation Data

Abstract In this paper, a robust online approach based on wavelet transform and matrix pencil (WTMP) is proposed to extract the dominant oscillation mode and parameters (frequency, damping, and mode shape) of a power system from wide-area measurements. For accurate and robust extraction of parameters, WTMP is verified as an effective identification algorithm for output-only modal analysis. First, singular value decomposition (SVD) is used to reduce the covariance signals obtained by natural excitation technique. Second, the orders and range of the corresponding frequency are determined by SVD from positive power spectrum matrix. Finally, the modal parameters are extracted from each mode of reduced signals using the matrix pencil algorithm in different frequency ranges. Compared with the original algorithm, the advantage of the proposed method is that it reduces computation data size and can extract mode shape. The effectiveness of the scheme, which is used for accurate extraction of the dominant oscillation mode and its parameters, is thoroughly studied and verified using the response signal data generated from 4-generator 2-area and 16-generator 5-area test systems.

The Matrix Pencil for Power System Modal Extraction

2005 ◽  
Vol 20 (1) ◽  
pp. 501-502 ◽  
Author(s):  
M.L. Crow ◽  
A. Singh
Keyword(s):  
Power System ◽  
Matrix Pencil ◽  
The Matrix

scholarly journals SVD-Based Screening for the Graphical Lasso

2017 ◽  
Author(s):  
Yasuhiro Fujiwara ◽  
Naoki Marumo ◽  
Mathieu Blondel ◽  
Koh Takeuchi ◽  
Hideaki Kim ◽  
...  
Keyword(s):  
Data Matrix ◽  
Computation Cost ◽  
Original Algorithm ◽  
Graphical Lasso ◽  
Large Size ◽  
Fast Approach ◽  
High Dimension Data ◽  
Inverse Covariance Matrix ◽  
The Matrix ◽  
Value Decomposition

The graphical lasso is the most popular approach to estimating the inverse covariance matrix of high-dimension data. It iteratively estimates each row and column of the matrix in a round-robin style until convergence. However, the graphical lasso is infeasible due to its high computation cost for large size of datasets. This paper proposes Sting, a fast approach to the graphical lasso. In order to reduce the computation cost, it efficiently identifies blocks in the estimated matrix that have nonzero elements before entering the iterations by exploiting the singular value decomposition of data matrix. In addition, it selectively updates elements of the estimated matrix expected to have nonzero values. Theoretically, it guarantees to converge to the same result as the original algorithm of the graphical lasso. Experiments show that our approach is faster than existing approaches.

scholarly journals Locating Ships Using Time Reversal and Matrix Pencil Method by Their Underwater Acoustic Signals

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5065
Author(s):  
Daniel Chaparro-Arce ◽  
Sergio Gutierrez ◽  
Andres Gallego ◽  
Cesar Pedraza ◽  
Felix Vega ◽  
...  
Keyword(s):  
Time Reversal ◽  
Communication Channel ◽  
Matrix Pencil ◽  
Acoustic Signals ◽  
Underwater Acoustic ◽  
Low Bit Rate ◽  
Matrix Pencil Method ◽  
The Matrix ◽  
Resonance Expansion ◽  
Passive Sensors

This paper presents a technique, based on the matrix pencil method (MPM), for the compression of underwater acoustic signals produced by boat engines. The compressed signal, represented by its complex resonance expansion, is intended to be sent over a low-bit-rate wireless communication channel. We demonstrate that the method can provide data compression greater than 60%, ensuring a correlation greater than 93% between the reconstructed and the original signal, at a sampling frequency of 2.2 kHz. Once the signal was reconstituted, a localization process was carried out with the time reversal method (TR) using information from four different sensors in a simulation environment. This process sought to achieve the identification of the position of the ship using only passive sensors, considering two different sensor arrangements.

scholarly journals Internet Photogrammetry for Inspection of Seaports

2017 ◽  
Vol 24 (s1) ◽  
pp. 174-181 ◽  
Author(s):  
Zygmunt Paszotta ◽  
Malgorzata Szumilo ◽  
Jakub Szulwic
Keyword(s):  
Fundamental Matrix ◽  
Laser Scanning ◽  
3D Models ◽  
Digital Cameras ◽  
Correct Orientation ◽  
The Matrix ◽  
Transport Safety ◽  
Left And Right ◽  
Value Decomposition ◽  
The Relationship

Abstract This paper intends to point out the possibility of using Internet photogrammetry to construct 3D models from the images obtained by means of UAVs (Unmanned Aerial Vehicles). The solutions may be useful for the inspection of ports as to the content of cargo, transport safety or the assessment of the technical infrastructure of port and quays. The solution can be a complement to measurements made by using laser scanning and traditional surveying methods. In this paper the authors recommend a solution useful for creating 3D models from images acquired by the UAV using non-metric images from digital cameras. The developed algorithms, created and presented software allows to generate 3D models through the Internet in two modes: anaglyph and display in shutter systems. The problem of 3D image generation in photogrammetry is solved by using epipolar images. The appropriate method was presented by Kreiling in 1976. However, it applies to photogrammetric images for which the internal orientation is known. In the case of digital images obtained with non-metric cameras it is required to use another solution based on the fundamental matrix concept, introduced by Luong in 1992. In order to determine the matrix which defines the relationship between left and right digital image it is required to have at least eight homologous points. To determine the solution it is necessary to use the SVD (singular value decomposition). By using the fundamental matrix the epipolar lines are determined, which makes the correct orientation of images making stereo pairs, possible. The appropriate mathematical bases and illustrations are included in the publication.

scholarly journals Linearization schemes for Hermite matrix polynomials

2015 ◽  
Author(s):  
Nikta Shayanfar ◽  
Heike Fassbender
Keyword(s):  
Eigenvalue Problem ◽  
Eigenvalue Problems ◽  
Matrix Polynomial ◽  
Multiple Input Multiple Output ◽  
Matrix Pencil ◽  
Least Square ◽  
Coupled Systems ◽  
Matrix Polynomials ◽  
Polynomial Eigenvalue Problem ◽  
The Matrix

The polynomial eigenvalue problem is to find the eigenpair of $(\lambda,x) \in \mathbb{C}\bigcup \{\infty\} \times \mathbb{C}^n \backslash \{0\}$ that satisfies $P(\lambda)x=0$, where $P(\lambda)=\sum_{i=0}^s P_i \lambda ^i$ is an $n\times n$ so-called matrix polynomial of degree $s$, where the coefficients $P_i, i=0,\cdots,s$, are $n\times n$ constant matrices, and $P_s$ is supposed to be nonzero. These eigenvalue problems arise from a variety of physical applications including acoustic structural coupled systems, fluid mechanics, multiple input multiple output systems in control theory, signal processing, and constrained least square problems. Most numerical approaches to solving such eigenvalue problems proceed by linearizing the matrix polynomial into a matrix pencil of larger size. Such methods convert the eigenvalue problem into a well-studied linear eigenvalue problem, and meanwhile, exploit and preserve the structure and properties of the original eigenvalue problem. The linearizations have been extensively studied with respect to the basis that the matrix polynomial is expressed in. If the matrix polynomial is expressed in a special basis, then it is desirable that its linearization be also expressed in the same basis. The reason is due to the fact that changing the given basis ought to be avoided \cite{H1}. The authors in \cite{ACL} have constructed linearization for different bases such as degree-graded ones (including monomial, Newton and Pochhammer basis), Bernstein and Lagrange basis. This contribution is concerned with polynomial eigenvalue problems in which the matrix polynomial is expressed in Hermite basis. In fact, Hermite basis is used for presenting matrix polynomials designed for matching a series of points and function derivatives at the prescribed nodes. In the literature, the linearizations of matrix polynomials of degree $s$, expressed in Hermite basis, consist of matrix pencils with $s+2$ blocks of size $n \times n$. In other words, additional eigenvalues at infinity had to be introduced, see e.g. \cite{CSAG}. In this research, we try to overcome this difficulty by reducing the size of linearization. The reduction scheme presented will gradually reduce the linearization to its minimal size making use of ideas from \cite{VMM1}. More precisely, for $n \times n$ matrix polynomials of degree $s$, we present linearizations of smaller size, consisting of $s+1$ and $s$ blocks of $n \times n$ matrices. The structure of the eigenvectors is also discussed.

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