The present invention proposes a method, device and
system for identifying, diagnosing and predicting abnormalities of massive data streams in a dispatching
automation system. The method comprises the following steps: a lossless acquisition step, in which the power dispatching data is losslessly acquired and preprocessed to obtain a message information
stream; a sample expansion step, in which the message information
stream is further preprocessed and WGAN-GP is performed for sample expansion; an AI identification step, in which the expanded samples are input into a two-
step detection method composed of a PCA+XGBoost and CNN+LSTM joint
algorithm model to improve the problem of low recognition rate of traditional
machine learning and an output result step. The method, device and
system of the present invention do not require a large amount of manpower, are short in time, do not require a large amount of
labeled data, and have high accuracy.