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

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...

AI Technical Summary

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 and missed detection, the present invention provides a dynamic online sequence extreme learning machine arc fault identification The algorithm can adapt to different grid load environments through online learning

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