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Electric-energy quality data compression and reconstruction method based on self-adaptive dictionary learning

An adaptive dictionary and power quality technology, applied to electrical components, code conversion, etc., can solve problems such as the matching problem between power quality data and sparse transformation base, and achieve improved sampling efficiency and time, simple compressed sampling, and reduced The effect of data redundancy

Active Publication Date: 2016-08-03
JIANGSU UNIV
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Problems solved by technology

However, in the process of compressing and reconstructing power quality data based on compressed sensing theory, Fourier orthogonal transform bases or general dictionaries such as DCT and DWT are generally used to sparsely represent power quality data, without considering power quality data and sparse transformation. The Matching Problem of Basis (Sparse Transformation Dictionary)

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  • Electric-energy quality data compression and reconstruction method based on self-adaptive dictionary learning

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specific Embodiment approach

[0028] combine figure 1 Illustrate the specific embodiment of the present invention, the steps are as follows:

[0029] Step 1. Establish different types of power quality data signal models, generate a large amount of power quality data, and form a power quality signal training sample set.

[0030] The power quality data signal model established by the present invention using MATLAB simulation includes normal power signal, steady state power quality signal, and transient power quality signal, a total of 8 types: normal voltage, short-term harmonic, voltage transient oscillation, voltage transient State pulses, voltage swells, voltage dips, voltage interruptions and voltage gaps. The simulation produces a large number of power quality data signals to form a training sample set as E=[E 1 ,E 2 ,...,E 8 ]∈R M×W , where W is the total number of training samples, the fundamental frequency of the power quality signal is power frequency 50Hz, the sampling rate is 3200, and the sa...

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Abstract

The invention an electric-energy quality data compression and reconstruction method based on self-adaptive dictionary learning, and self-adaptive dictionary learning is used for sparse representation of electric-energy quality data. A lot of different types of electric energy quality data form a training sample set, and the completeness and redundancy of the training sample set are ensured; basic atom sparse codes which can represent the electric energy quality data is extracted from the training sample set adaptively, and a self-adaptive dictionary is obtained via iteration; a random Gauss matrix is used to carry out dimension-reducing measurement on electric energy quality test signals, and compression sampling is realized; and on the basis of compressed perception theories, the self-adaptive dictionary is used for sparse solution to obtain a sparse representation matrix of test signals, and the original signals are decoded and reconstructed. Thus, the electric energy quality data is compressed and sampled simply and reconstructed accurately, the sampling efficiency of the electric energy quality data is improved, and storage of redundant data is reduced.

Description

technical field [0001] The invention belongs to the field of power system power quality data compression and reconstruction research, in particular to a power quality data compression and reconstruction method based on adaptive dictionary learning. Background technique [0002] With the expansion of power grid scale, network integration, and the development of electrical informatization, on the one hand, the automation and informatization level of power system operation management has been improved. burden. In-depth study of power quality data compression and reconstruction technology is of great significance to reduce the burden of redundant storage of power quality data, improve the real-time performance of power data transmission, and accelerate the development of power system informatization. Power system power quality data compression and reconstruction has become a new research topic. [0003] According to Shannon's sampling law, the signal can be accurately reconstr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M7/30
Inventor 沈跃张瀚文刘国海刘慧
Owner JIANGSU UNIV
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