Gramian-based reduction method applied to large sparse power system descriptor models

Abstract The noise types of power system intelligent alarm data are complex. When reducing the intelligent alarm data, the profile noise statistics of the noise data are large, resulting in the actual noise reduction value is too small. To solve this problem, a power system intelligent alarm data noise reduction method based on singular value decomposition is designed. The selected normalized decomposition matrix iteratively processes the original matrix, the singular value decomposes the power system alarm data, sets an estimation quantity within the paradigm of the alarm data, controls the noise profile noise statistics, characterizes the noise alarm data structure, uses the SC algorithm to process the cluster basis vectors in the noise data structure, and constructs a repeated iterative convergence process to realize intelligent data noise reduction processing. The original alarm data within a known power system is used as test data, the power system alarm window is set, and the power system alarm data singular values are circled. The data mining-based alarm data noise reduction method, the regularized filter-based alarm data noise reduction method and the designed data noise reduction method are applied to the noise reduction process, and the results show that the designed data noise reduction method has the largest noise value and the best noise reduction effect.

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Excitation Control of Generators for Stabilizing a Power System of Large Scale by the Model Reduction Method

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes1990.116.1_104 ◽

1996 ◽

Vol 116 (1) ◽

pp. 104-113

Author(s):

Kunio Matusita ◽

Fujio Isiguro ◽

Sigeru Omatu

Keyword(s):

Power System ◽

Model Reduction ◽

Large Scale ◽

Reduction Method ◽

Excitation Control

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A novel second harmonic current reduction method for dual active bridge used in photovoltaic power system

2013 IEEE Energy Conversion Congress and Exposition ◽

10.1109/ecce.2013.6646902 ◽

2013 ◽

Cited By ~ 5

Author(s):

Wentao Wang ◽

Xinbo Ruan ◽

Xuehua Wang

Keyword(s):

Power System ◽

Reduction Method ◽

Second Harmonic ◽

Harmonic Current ◽

Dual Active Bridge ◽

Photovoltaic Power ◽

Current Reduction

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Nonlinear Dynamic Power System Model Reduction Analysis Using Balanced Empirical Gramian

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.448-453.2368 ◽

2013 ◽

Vol 448-453 ◽

pp. 2368-2374 ◽

Cited By ~ 2

Author(s):

Hong Shan Zhao ◽

Ning Xue ◽

Ning Shi

Keyword(s):

Nonlinear System ◽

Power System ◽

Model Reduction ◽

Reduction Method ◽

System Model ◽

System Dynamic Model ◽

Lower Dimension ◽

Input And Output ◽

System Input ◽

Power System Model

This paper presents an empirical Gramian balanced reduction method which efficiently solves nonlinear power system model reduction problems. This method projects the nonlinear power system dynamic model to a lower dimension subspace and the reduced model can retain the original nonlinear system input and output dynamic behaviors. A four-generator nonlinear power system, combining different factors which form empirical controllable and observable Gramian matrices, is used to analyze how external excitations and outputs affect balanced empirical Gramian reduction. The case verifies the feasibility and effectiveness of balanced empirical Gramian reduction method.

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