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Method for analyzing power system fault recording data based on Marla algorithm

A fault recording and power system technology, applied in the field of data analysis, can solve problems such as large model errors and inability to detect non-integer harmonics

Inactive Publication Date: 2013-04-03
ELECTRIC POWER RES INST OF GUANGDONG POWER GRID +2
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Problems solved by technology

Fourier analysis can only identify integer harmonics of the fundamental frequency, but cannot detect non-integer harmonics, so due to the existence of non-integer harmonics, the results calculated by Fourier analysis will be mistaken for integer Subharmonic results, so that the whole calculation has a large model error

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  • Method for analyzing power system fault recording data based on Marla algorithm
  • Method for analyzing power system fault recording data based on Marla algorithm
  • Method for analyzing power system fault recording data based on Marla algorithm

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Embodiment Construction

[0040] Such as figure 1 As shown, the power system fault recording data analysis method of the present invention comprises the following steps:

[0041] 1. Sampling of wave recording data

[0042] The number of sampling points for each cycle is N=128, and the corresponding sampling frequency f s =Nf f =128*50=6400Hz;

[0043] 2. Extraction of transient waveform

[0044] In Mara's algorithm, the wavelet base consists of a scaling function The linear combination after translation and stretching is formed, and its construction process is actually the design process of low-pass filter G(ω) and high-pass filter H(ω); the discrete wavelet transform based on Mara algorithm adopts high-pass filter (wavelet filter ) and low-pass filtering (scale filtering), the frequency spectrum of the fault recording signal is separated into high-frequency bands (wavelet coefficients) and low-frequency bands (approximate coefficients) at the first scale.

[0045] 3. Scale analysis

[0046]Sup...

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Abstract

The invention relates to a method for analyzing power system fault recording data based on the Marla algorithm, which comprises the following steps of: 1, sampling recording data, wherein the sampling point number N in each period is 128, and the corresponding sampling frequency is fs=Nff=128*50=6400Hz; 2, extracting transient waveform; 3, analyzing the scale; 4, decomposing and reconstructing the Marla algorithm: carrying out five-layer multi-resolution analysis of a signal, detecting the mutational site of the signal by utilizing wavelet transform, and detecting a modulus maximum; and 5, analyzing fault duration and fault waveform. The method disclosed by the invention is capable of greatly reducing the calculation amount of the wavelet transform and well analyzing a power system fault process and extracting fault feature genes, and is beneficial to processing the power system fault signal containing large amounts of information in real time, therefore, diagnosis and location of a fault are carried out.

Description

technical field [0001] The invention relates to a data analysis method, in particular to a power system fault recording data analysis method based on the Mara algorithm. Background technique [0002] Digital fault recorders are generally installed in power systems to monitor the voltage and current signals of transmission lines, distribution lines, transformers and other equipment, and to record faults, voltage dips and switching events in the power system. Post-event analysis of the disturbance in the wave recording data is necessary. For example, determine the time of occurrence of faults and switching events, fault duration and location, the nature and type of faults, and evaluate the performance of relays and circuit breakers, etc. However, digital fault recorders usually record a large amount of non-transient data, so it is very difficult to check the fault transient data among the numerous waveform data. [0003] The identification, processing and utilization of tran...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/08
Inventor 高新华陶维青余南华李林陈炯聪何刚李传健郭晋楠李瑞单开周克林柳慧超黄向明
Owner ELECTRIC POWER RES INST OF GUANGDONG POWER GRID
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