This invention discloses a method and
system for real-time dynamic compensation measurement of
air volume in large wind turbines based on
deep learning, comprising: S1: real-time acquisition of initial data of the large wind
turbine through preset multiple types of sensors; S2: optimization and synchronization alignment of the initial data of the wind
turbine through industrial IoT edge nodes to obtain a multi-dimensional time-
series data matrix with operating condition labels; S3: based on the multi-dimensional time-
series data matrix, generating dynamic compensation features including
eddy current suppression coefficients through an improved CNN-LSTM
hybrid network; S4: decoding the optimal dynamic compensation
air volume based on the dynamic compensation features through an adaptive reinforcement
ensemble learning framework, and triggering a
PID controller based on the optimal dynamic compensation
air volume to realize dynamic compensation measurement of air volume. For wind
turbine air volume, which is affected by
multiple factors and has a certain degree of complexity, more accurate prediction can be achieved.