A fault prediction framework for Doubly-fed induction generator under time-varying operating conditions driven by digital twin

Abstract

Accurate status prediction is critical for detecting potential failures of Doubly-fed induction generator (DFIG). A unique Digital Twin (DT) fault prediction paradigm is put forth in this research, which uses an edge intelligence paradigm to ensure high-performance fault prediction. Edge-based DT provides compute and storage capabilities on edge devices to enable effective data processing. The stator current spectrum and instantaneous power spectrum of the DFIG are simulated and assessed, followed by the development of a verification prototype on the DFIG and the qualitative observation of the internal performance of the DFIG. The encoder is constructed using Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) with the channel attention mechanism, and the decoder is constructed based on a long short-term memory (LSTM) network to form the self-encoding model named SE-CNN-LSTM. In order to forecast problems and provide a foundation for predictive maintenance, the status indicator is built based on the prediction residuals of the status variables. Based on the outcomes of edge-cloud co-simulation and data collected from the Supervisory Control and Data Acquisition system at the Da Bancheng Wind Farm in Xinjiang, China, it is demonstrated that the framework and technology are workable and capable of predicting generator failures.