AUTHOR=Xing Chao , Xi Xinze , He Xin , Liu Mingqun 

TITLE=Generator condition monitoring method based on SAE and multi-source data fusion

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1308957

DOI=10.3389/fenrg.2023.1308957

ISSN=2296-598X

ABSTRACT=With the increasing number of units involved in power system regulation and the increasing proportion of industrial load, a single data source has been unable to meet the accuracy requirements of on-line monitoring of unit condition in the new power system. Based on stacked autoencoder (SAE) network, combined with multi-source data fusion technology and adaptive threshold, a generator condition monitoring method is proposed. Firstly, a SCADA-PMU data fusion method based on weighted D-S evidence theory is proposed. Then, the auto-coding technology is introduced to build a stacked selfcoding deep learning network model, extract the deep features of the training data set, and build a generator fault detection model. Finally, by smoothing the reconstruction error and combining with the trend change of the state monitoring quantity detected by the adaptive threshold, the fault judgment is realized. The simulation results show that, compared with the traditional method based on a single data source, the proposed method has higher robustness and accuracy, thus effectively improving the refinement level of generator condition monitoring.