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
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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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