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A Novel Electrical Equipment Status Diagnosis Method Based on Super-Resolution Reconstruction and Logical Reasoning
Peng Ping1,2, Qida Yao2, Wei Guo3
1College of Aerospace Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|July 13, 2024
Summary
This study introduces a novel method for accurate electrical equipment fault diagnosis by combining Generative Adversarial Networks (GANs) for noise reduction and deep learning for spatial-temporal analysis, enhancing power system reliability.
Area of Science:
- Electrical Engineering
- Artificial Intelligence
- Power Systems
Background:
- Accurate detection of electrical equipment states and faults is vital for power system reliability.
- Deep learning visual inspection methods face challenges from environmental noise and limited logical reasoning capabilities.
Purpose of the Study:
- To develop an interpretable fault diagnosis method for electrical equipment.
- To address environmental noise and enhance situational awareness in power systems.
Main Methods:
- Image super-resolution reconstruction using Generative Adversarial Networks (GANs) to filter noise.
- Deep learning analysis of pixel information to extract spatial features.
- Construction of logic diagrams for electrical equipment clusters to integrate spatial and temporal states.
Main Results:
- The proposed method effectively filters environmental noise from equipment images.
- Spatial features and temporal states are integrated for comprehensive analysis.
- High accuracy in diagnosing electrical equipment faults was demonstrated across six datasets.
Conclusions:
- The developed interpretable fault diagnosis method significantly improves the accuracy of electrical equipment state recognition.
- The integration of GANs and deep learning offers a robust solution for complex power system monitoring and fault prediction.
