Abstract: A method and a system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces are disclosed, which belong to the field of fault diagnosis of power equipment. The method includes: test samples containing different locations, types and severity levels of fault of power equipment are acquired to form a real physical space; a virtual mirror space is acquired by simulation according to a simulation model of the equipment to be diagnosed; the training set in the real physical space is spatially integrated with the sample set in the virtual mirror space to obtain a training sample set in the twin spaces; the training sample set in the twin spaces serves as the supplement to the training set in the real physical space, and the fault type and fault location serve as diagnostic labels to be input to the deep neural network for training.

Abstract: Provided are a method and apparatus for estimating a meteorology sensitive load power. The method includes: obtaining a meteorology sensitive load power estimation model; inputting a daily load curve of a date to be estimated to the meteorology sensitive load power estimation model and extracting a daily load curve dimension reduction feature of the date to be estimated; and outputting a meteorology sensitive load power based on the daily load curve dimension reduction feature of the date to be estimated and mapping relationships from daily load curve dimension reduction features onto meteorology sensitive load powers. The proposed estimation model can directly obtain the meteorology sensitive load power curve from the daily load curve, and is especially applicable to cases where meteorology data is frequently lost in practical applications.