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

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. 一种基于动态在线序列的极限学习机的串联电弧故障识别方法，包括以下步骤：1)电能波形采样数据