Fault arc online detecting method based on wavelet first-layer high-frequency component correlation

A high-frequency component and fault arc technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problems of short fault judgment time, long learning process, lack of versatility, etc., to achieve small calculation amount and convenient data Effects of computation and efficient transfer, increasing versatility and effectiveness

Inactive Publication Date: 2015-05-06
SHANGHAI JIAO TONG UNIV
View PDF4 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The time-domain method is usually combined with other intelligent algorithms. Some scholars use the learning ability of the Kalman filter to judge whether the arc occurs by calculating the difference between the acquired signal and the actual signal in the time domain. It has certain accuracy, but its learning relatively long process
There is also a judgment by comparing the current peak value or average value of each half cycle during normal and fault conditions, but it is limited to certain specific loads and lacks versatility
[0006] For different loads, the existing AC series fault arc discrimination algorithm can usually only be effective in a certain situation such as the current average value or peak value, the frequency domain distribution frequency band, a certain harmonic in the frequency domain, and the amplitude of wavelet high-frequency details. Good application effect, but in the actual application of low-voltage series arc fault circuit breaker products, due to the diversity and complexity of the load, it is difficult to achieve accurate, reliable, and universal judgment results and short fault judgment time achieved by a single method To meet the real-time requirements of online detection

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Fault arc online detecting method based on wavelet first-layer high-frequency component correlation
  • Fault arc online detecting method based on wavelet first-layer high-frequency component correlation
  • Fault arc online detecting method based on wavelet first-layer high-frequency component correlation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] The used method structure diagram and program flow chart of the present embodiment are as follows figure 1 , figure 2 As shown, the load is a 1000W electric kettle resistive load, and the specific steps for fault arc identification are as follows:

[0051] 1) According to the sampling rate f=10 4 Hz collects the series current f(t) of the circuit where the load is located.

[0052] 2) The amplitude of the sampling current f(t) is A m , normalize f(t) to get Remove the external noise in the signal, and the normalized current sequence Carry out software noise reduction, get g(t)=[x 1 ,x 2 ,...,x N ].

[0053] 3) Select the db1 wavelet base ψ(t) in the Daubechies wavelet family to perform wavelet decomposition on g(t), Is a pulse function with a width of 1 and a height of 1, and calculates the high-frequency detail coefficient Obtain the high-frequency detail sequence D1 of g(t).

[0054] 4) Take the current of 5 cycles when the circuit operates norma...

Embodiment 2

[0065] Figure 5 It is the judging process of the fault arc when the detection method of the present invention is inductively loaded with a 500W electric drill. The specific steps of arc fault identification are the same as those in Embodiment 1. Figure 5 From top to bottom in the middle is the sampling current g(t) after the normalized noise reduction of the electric drill load, the high-frequency component sequence D1 of g(t), and the sequence to be measured y i with reference sequence s j The correlation coefficient ξ ij , the trip signal flag of the circuit where the load is located is Trip. Depend on Figure 5 It can be seen that the algorithm of the present invention can realize the accurate identification and judgment of the fault arc in the electric drill with inductive load.

Embodiment 3

[0067] Figure 6 It is the judging process of the fault arc when the detection method of the present invention is used for a capacitive load of a 50W capacitive start-up fan. The specific steps of arc fault identification are the same as those in Embodiment 1. Figure 6 From top to bottom in the figure are the sampling current g(t) after the normalized noise reduction of the capacitive start-up fan load, the high-frequency component sequence D1 of g(t), and the sequence to be measured y i with reference sequence s j The correlation coefficient ξ ij , the trip signal flag of the circuit where the load is located is Trip. Depend on Figure 6 It can be seen that the algorithm of the present invention can realize the accurate identification and judgment of the fault arc when the capacitive load capacitor starts the fan.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a fault arc online detecting method based on wavelet first-layer high-frequency component correlation. The method comprises the following steps of 1) extracting the first-layer wavelet decomposition high-frequency component of series current during normal operation of a load to obtain the reference sequence of sampling current; 2) collecting the sampling current of a low-voltage AC (alternate current) series circuit, where the load is disposed, at a set frequency, performing wavelet decomposition on the sampling current to extract a first-layer high-frequency detail component; 3) segmenting the first-layer high-frequency detail component extracted in the step 2) to obtain a sequence to be detected; 4) according to the correlation coefficient of the sequence to be detected and the reference sequence, determining whether fault arcs exist, if so, performing the step 5), and if not, returning to the step 2); 5) determining whether the number of the fault arcs meets the UL1699 standards, if so, outputting tripping signals. Compared with the prior art, the fault arc online detecting method based on wavelet first-layer high-frequency component correlation overcomes the dependency of existing low-voltage AC series fault arc determining methods on specificity of the load and shortens the fault determining time.

Description

technical field [0001] The invention relates to a low-voltage AC series fault arc discrimination method, in particular to an on-line fault arc detection method based on the correlation analysis of wavelet one-layer high-frequency components. Background technique [0002] Long-term load or overload operation of the circuit will cause partial discharge due to damaged and aging line insulation and poor electrical contact, which will cause arcing. The local resistance of the arc increases, and the released energy generates heat, which aggravates the deterioration of the circuit and ignites the 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 analysis of time domain and frequency domain characteristics of fault arc current and voltage signals in the circuit. [0004] In the frequency domain method, some scholars proposed to use the fast Fourier analysis method to take the amplit...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/00
Inventor 汪洋堃张峰张士文杨光
Owner SHANGHAI JIAO TONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap