Dynamically enhanced weighted network acoustic and photoelectric GIS fault diagnosis based on attention mechanism

In power systems, the normal functioning of gas-insulated switchgear (GIS) is essential for the security of the electrical grid.However, when a single signal is used for discharge detection and diagnosis, it will be interfered.Through joint analysis of different signals, fault diagnosis can be more accurately performed.Therefore, to address this problem, this paper proposes a dynamically enhanced weighted network model (AMB-DEWNM) based on the attention mechanism.The model first extracts fault features from the PRPD spectra of UHF, optical and ultrasonic signals through a multi-scale convolutional neural grid.Furthermore, a two-tier focus module is introduced to enhance fault characteristics that are insensitive to changes in operating conditions.Finally, a new dynamic enhanced weighted voting strategy (DEWVS) is designed.This strategy constructs a diagnostic performance index matrix by considering the diagnostic accuracy and misclassification rate of the base model to dynamically adjust the voting weight of each base model.distribution to obtain more reliable collaborative diagnostic results.Test outcomes demonstrate that the error detection precision of the AMB-DEWNM system is notably enhanced.Compared with other advanced network models, the diagnosis accuracy is as high as 95.28%.It has high stability and robustness, and provides fault detection and maintenance for GIS.strong support.