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Separation and recognition algorithm for transformer oiled paper insulation multiple partial discharging source signals

A transformer oil and identification algorithm technology, applied in the direction of testing dielectric strength, etc., can solve the problem of inability to identify and diagnose signals of multiple partial discharge sources

Active Publication Date: 2013-05-08
STATE GRID CHONGQING ELECTRIC POWER CO ELECTRIC POWER RES INST +2
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, it is impossible to directly identify and diagnose multiple partial discharge source signals by using the PRPD spectral features or parameters of a traditional single discharge source, and it is necessary to pulse-separate the received superimposed signals of multiple discharge sources before identifying and diagnosing the discharge source type

Method used

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  • Separation and recognition algorithm for transformer oiled paper insulation multiple partial discharging source signals
  • Separation and recognition algorithm for transformer oiled paper insulation multiple partial discharging source signals
  • Separation and recognition algorithm for transformer oiled paper insulation multiple partial discharging source signals

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Embodiment 1

[0053] figure 1 It is a multi-partial discharge source identification flow chart, as shown in the figure: a transformer oil-paper insulation multi-partial discharge source signal separation and identification algorithm provided by the present invention includes the following steps:

[0054] S1: Collect multiple partial discharge source signals and single partial discharge source signals;

[0055] S2: Analyze multiple partial discharge source signals to obtain single PD pulse group and phase-amplitude matrix data;

[0056] S3: Calculate the similarity of a single PD pulse group and generate a similarity matrix;

[0057] S4: Clustering the phase amplitude matrix data combined with the similarity matrix through neighbor propagation clustering and generating subclass pulse PRPD map data;

[0058]S5: Identify subtype pulse PRPD spectrum data according to the PRPD spectrum fingerprint feature of a single partial discharge source signal and separate multiple partial discharge sourc...

Embodiment 2

[0089] The difference between this embodiment and embodiment 1 is only:

[0090] In this embodiment, three artificial models are designed for simulating transformer oil-paper insulation defects: creeping discharge model, corona discharge model and interlayer discharge model.

[0091] figure 2 It is the PRPD spectrum after artificially synthesizing the signals of creeping discharge and corona discharge. The sampling voltage is 22kV, and the sampling rate is 100MS / s, including 567 pulses of creeping discharge and 235 pulses of corona discharge.

[0092] The first step of the algorithm is to calculate the similarity matrix, and the dimension of the S-transform magnitude matrix (STA) is 100×200. The damping factor λ=0.5 is selected to prevent the oscillation in the nearest neighbor propagation clustering algorithm (APC). At the same time, set the reference vector p as p(1)=p(2)=...=p(i)=...=p(802)=-20. After iterative operation, the APC algorithm automatically gives the separa...

Embodiment 3

[0095] The difference between this embodiment and embodiment 1 is only:

[0096] Figure 4 The PRPD spectrum after artificial synthesis of corona and interlayer discharge signals is given. The sampling voltage of corona discharge is 19kV, the sampling voltage of interlayer discharge is 10kV, and the sampling rate is 100MS / s. The collected signals contain 416 Corona discharge pulses and 854 interlayer discharge pulses.

[0097] Also select the damping factor λ=0.5, and set the reference vector p to p(1)=p(2)=...=p(i)=...=p(802)=-20. After iterative operation, the separation results obtained by the APC algorithm are as follows: Figure 5 shown. The number of pulses in group 1 and group 2 are 411 and 859, respectively. By observation, the pulses in group 1 mainly come from corona discharge, while the pulses in group 2 mainly come from interlayer discharge. The correct rate of separation of PD signal was calculated to be 98.35%.

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Abstract

The invention discloses a separation and recognition algorithm for transformer oiled paper insulation multiple partial discharging source signals. According to the separation and recognition algorithm, time-frequency analysis and close-neighbor similar transmission clusters are adopted to conduct separation and recognition of the multiple partial discharging source signals. The separation and recognition algorithm comprises the steps of firstly, using S transformation to conduct the time-frequency analysis for partial discharging pulses, obtaining an S transformation amplitude (STA) matrix, calculating similarity of the pulses, secondly, using the similarity matrix to conduct the close-neighbor transmission cluster, achieving automatic separation of the discharging pulses, finally, extracting fingerprint feature recognition discharging source modes of a pulse phase partial distribution (PRPD) pattern map of subclass pulses, and using the multiple discharging source signals which are collected through tests and combined by manual work for verification of effectiveness of the separation and recognition algorithm. According to the separation and recognition algorithm for the transformer oiled paper insulation multiple partial discharging source signals, the number of the clusters and corresponding pulse groups can be provided according to the similarity among the pulses and free of influence of the pulse width. When certain pulse width is used for extracting a single pulse wave form for collected PD original data, separation of the multiple discharging sources can be well achieved.

Description

technical field [0001] The invention relates to the field of on-line monitoring and fault diagnosis of electrical equipment insulation, in particular to an algorithm for separating and identifying pulse signals of transformer oil-paper insulation multi-partial discharge sources. Background technique [0002] Power transformers are the core equipment in the power grid. Partial discharge (PD) caused by insulation defects will inevitably occur during equipment manufacturing, transportation and long-term operation, which seriously affects the operational reliability of transformers. Therefore, the analysis and diagnosis of partial discharge source information is an important content in transformer state assessment, which provides a reference for formulating reasonable maintenance and repair strategies. [0003] Traditional methods often use the pulse phase distribution pattern (PRPD) spectrum shape of the discharge pulse signal and the characteristic parameters extracted from it...

Claims

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Application Information

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IPC IPC(8): G01R31/12
Inventor 王谦徐瑞林廖瑞金郭超汪可逄凯李勇杨雁齐超亮
Owner STATE GRID CHONGQING ELECTRIC POWER CO ELECTRIC POWER RES INST
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