Series arc fault identification method of extreme learning machine based on dynamic online sequence

An extreme learning machine and series arc technology, applied in neural learning methods, pattern recognition in signals, character and pattern recognition, etc., can solve the problems of lack of good adaptability and low recognition accuracy, and save cloud computing power, The effect of high recognition accuracy and improved accuracy

Pending Publication Date: 2022-02-11
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the low identification accuracy in the existing arc fault identification process, the lack of good adaptability when the load status changes, and the many cases of false detection an

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  • Series arc fault identification method of extreme learning machine based on dynamic online sequence
  • Series arc fault identification method of extreme learning machine based on dynamic online sequence
  • Series arc fault identification method of extreme learning machine based on dynamic online sequence

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Embodiment Construction

[0039] The implementation of the present invention is described in detail in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following Example.

[0040] refer to figure 1 , a kind of series arc fault recognition method based on the extreme learning machine of dynamic online sequence, described method comprises the following steps:

[0041] Step 1) Current waveform sampling data noise reduction

[0042] The high-speed sampling mutual inductance sensor is used to obtain the waveform sampling data of the current in the power grid. Due to interference and other reasons, the data has glitches. Three adjacent sampling points are used as a group, and the pre-average filtering algorithm is adopted. The average value of this group replaces...

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Abstract

The invention discloses a series arc fault identification method of an extreme learning machine based on a dynamic online sequence. The method comprises the following steps: 1) noise reduction of electric energy waveform sampling data; 2) real-time data segmenting and intercepting; 3) waveform data calculating and processing; and 4) ELM fault arc identification: based on an ELM algorithm, arc identification is converted into a fault classification problem, and weights from an input layer to a hidden layer are given randomly; after the weights from the input layer to the hidden layer exist, weights from the hidden layer to an output layer are obtained according to a least square method, and thus fault arc identification is realized. Through the dynamic online ELM learning algorithm which is efficient in calculation and high in universality, an accurate and effective way is provided for series arc fault identification of a power grid under different load conditions.

Description

technical field [0001] The invention relates to the field of arc fault identification, and relates to a method for identifying series arc faults based on an extreme learning machine (ELM) of a dynamic online sequence. Background technique [0002] The arc fault identification technology collects various parameters in the line, and uses different methods to analyze one or more parameters, so as to determine whether there is an arc fault in the line. However, the characteristics of arc faults such as randomness, concealment, and complexity make it difficult to be identified. [0003] At present, there are three types of methods used by researchers at home and abroad to detect series arc faults. The first is a detection method based on a mathematical model. The second is a detection method based on the physical characteristics of the arc. The third is the detection method based on arc current and voltage waveform. Due to the complexity and randomness of arc faults, it is di...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06F17/14G06F17/13
CPCG06N3/049G06N3/08G06F17/142G06F17/148G06F17/13G06N3/048G06F2218/04G06F2218/08G06F2218/12G06F18/241
Inventor 薛鹏潘国兵欧阳静赵继凯钱浚杰邓伟芳
Owner ZHEJIANG UNIV OF TECH
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