Intelligent Diagnosis of High-Voltage Switchgear Based on GANs and DualLSTM-DropNet

As artificial intelligence technology continues to advance in the smart grid domain, it shows significant potential for optimizing online monitoring and intelligent diagnosis of high-voltage switchgear. Online monitoring and intelligent diagnosis involve the real-time collection of characteristic parameters to assess equipment health, enabling fault diagnosis, condition evaluation, and lifespan prediction through algorithmic integration. However, challenges such as limited data samples, insufficient data validity and correlation, reliance on single diagnosis algorithms, and suboptimal performance in practical applications restrict the accuracy and usability of fault diagnosis. In this work, we employ the GANs model to mitigate the issue of limited valid data samples. Additionally, we analyze the correlations among multi-feature variables affecting equipment health, develop a multi-factor fusion diagnosis algorithm, and propose a novel DualLSTM-DropNet model by improving the traditional LSTM model. The model's validity is experimentally verified, achieving data and algorithm optimization for intelligent diagnosis of high-voltage switchgear through artificial intelligence. This work offers valuable insights for implementing condition-based maintenance, predictive maintenance, and lifecycle management of high-voltage switchgear.