Research on intelligent equipment fault recognition method driven by multisource data of power grid meteorological environment

With the impact of climate change and the natural environment on power grid equipment, how to achieve reliable and stable operation of the power grid based on multisource data-driven means is an important issue at present. This paper proposes an intelligent equipment fault identification method driven by multi-source data of power grid meteorological environment. It constructs models for data collection, preprocessing, feature extraction, feature selection and multiple machine learning algorithms to achieve precise identification of power grid equipment faults. This method has great practical application value.