Doubly-fed fan control parameter identification method based on LSTM neural network

The invention discloses a doubly-fed fan controller parameter identification method based on LSTM (Long Short Term Memory), and the method comprises the steps: obtaining the hardware-in-the-loop test data of a doubly-fed fan controller through RT-LAB (Reverse Transcription-Laboratory), extracting the characteristic quantity with high correlation through a Person correlation coefficient method, carrying out the training of a neural network, carrying out the identification of the control parameters of a voltage outer loop and a current inner loop, and carrying out the recognition of the parameters of the doubly-fed fan controller. And the feasibility, effectiveness and practicability of the algorithm are tested through hardware-in-the-loop experimental data. Compared with a conventional parameter identification method, the method can simulate the operation characteristics of a fan control system through training historical sample data, and inputs actual measurement data to the LSTM neural network under the cond