Low-voltage AC arc fault detection method based on characteristic mode component energy analysis

A fault arc and energy analysis technology, which is applied in the direction of testing dielectric strength, can solve the problems of lack of versatility and difficulty in arc fault identification, so as to improve effective recognition, shorten fault judgment process time, and facilitate data calculation and efficient transmission Effect

Active Publication Date: 2015-11-18
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The existing arc fault discrimination algorithm is greatly affected by the load characteristics, and it can accurately identify the arc fault when the linear load is used, but it is difficult to identify the arc fault of the inductive load and switching power supply load that are widely used in modern electrical appliances, and lacks versatility.

Method used

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  • Low-voltage AC arc fault detection method based on characteristic mode component energy analysis
  • Low-voltage AC arc fault detection method based on characteristic mode component energy analysis
  • Low-voltage AC arc fault detection method based on characteristic mode component energy analysis

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Embodiment 1

[0051] In this embodiment, the above method is applied to the identification of series fault arcs with a resistive load of a 1000W electric kettle, and the specific steps are as follows:

[0052] 1) According to the sampling rate f=10 4 Hz collects the series current of the circuit where the load is located.

[0053] 2) Normalize the sampling current x(t) and perform software noise reduction to obtain y(t).

[0054] 3) Perform empirical mode decomposition on y(t) to obtain each eigenmode component IMF, and post-process the IMF to obtain the eigenmode component I 1 , as the fault characteristic analysis object.

[0055] 4) For the eigenmode component I 1 Carry out Hilbert transform to get I 1 The instantaneous amplitude distribution of .

[0056] 5) Calculate the eigenmode component I 1 The half cycle energy e j , its calculation formula is as follows:

[0057] e j = 1 N ...

Embodiment 2

[0066] In this embodiment, the detection method of the present invention is applied to the identification of a series fault arc of an electric drill with an inductive load of 500W, and the specific steps of identification are the same as those in Embodiment 1. image 3 It shows the judgment process of fault arc, from top to bottom is the electric drill load normalized noise reduction current y(t), eigenmode component I of y(t) 1 , I 1 The normalized half-period energy Power of the load, and the trip signal flag bit Trip of the branch where the load is located. Depend on image 3 It can be seen that the algorithm of the present invention can realize the accurate identification and judgment of series fault arcs in electric drills with inductive loads.

Embodiment 3

[0068] In this embodiment, the detection method of the present invention is applied to the identification of a series fault arc of a 50W capacitively activated fan with a capacitive load, and the specific steps of identification are the same as those in Embodiment 1. Figure 4 It shows the judgment process of fault arc, from top to bottom, the current y(t) and the eigenmode component I of y(t) after the normalized noise reduction of the capacitive start-up fan load 1 , I 1 The normalized half-period energy Power of the load, and the trip signal flag bit Trip of the branch where the load is located. Depend on Figure 4 It can be seen that the algorithm of the present invention can realize the accurate identification and judgment of the series fault arc when the capacitive load capacitor starts the fan.

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Abstract

The invention relates to a low-voltage AC arc fault detection method based on characteristic mode component energy analysis. The method includes the steps: utilizing empirical mode decomposition for analysis to obtain an intrinsic mode function of a sampled current, decomposing the intrinsic mode function to obtain the characteristic mode component I1 of an original sequence, extracting the instantaneous amplitude distribution of the characteristic mode component I1 through Hilbert transform, calculating energy of the half periods of the characteristic mode component I1, and identifying a series arc fault semi-cycle by comparing the energy of the half periods with a set reference threshold. Compared with the prior art, the method has the advantages of being high in identification, suitable for various loads, etc.

Description

technical field [0001] The invention relates to a detection method of a low-voltage AC series fault arc, in particular to a detection method of a low-voltage AC fault arc based on characteristic mode component energy analysis. Background technique [0002] Long-term load or overload operation of building electrical circuits, poor electrical contact, etc. will cause line insulation damage, aging, and carbonization, which will cause arc hidden dangers, increase local resistance of arcs, generate heat, aggravate line deterioration, and even ignite surrounding objects. [0003] Most of the built-in algorithms of low-voltage arc fault circuit interrupters (AFCI) at home and abroad are based on the time domain and frequency domain characteristic analysis of fault arc current and voltage signals in the circuit. [0004] When investigating the time-domain characteristics of electrical quantities, some scholars use the learning ability of the Kalman filter to judge whether an arc occ...

Claims

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Application Information

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IPC IPC(8): G01R31/12
Inventor 汪洋堃张峰张士文杨光
Owner SHANGHAI JIAO TONG UNIV
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