Transformer Multi-Dimensional Monitoring System
The Transformer Multi-Dimensional Intelligent Monitoring System provides real-time acquisition of transformer operating conditions, addressing both transient sudden faults inside the transformer and slowly developing latent failures. It enables comprehensive monitoring of transformers from multiple perspectives, including acoustic, electrical, oil, and mechanical dimensions.
The system host is equipped with built-in machine learning models for partial discharge analysis, acoustic fingerprint recognition, and vibration mechanism learning algorithms. It can be applied to various scenarios such as equipment condition monitoring, health management, and predictive maintenance in the power industry.
System Architecture
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Multi-dimensional comprehensive monitoring for full perception of equipment status
Covers various monitoring methods such as partial discharge, acoustic vibration, core grounding current, infrared dual-spectrum, and (oil and gas) monitoring, comprehensively capturing sudden transient faults and slowly developing latent faults inside transformers;
Support for acoustic-electric joint analysis
Integrates acoustic-electric joint analysis methods for ultra-high frequency, radio frequency, high frequency, and ultrasonic signals, which can effectively remove external discharge interference, internal electromagnetic noise interference, and on-site vibration interference, significantly improving the detection accuracy of partial discharge signals;
Support for channel self-inspection to ensure reliable data collection
Supports self-inspection functions for each acquisition channel, real-time monitoring of phenomena such as disconnection and abnormal interference in each channel;
Flexible configuration with modular acquisition, flexible configuration, and high scalability
The product adopts a combinable acquisition scheme, which can flexibly configure and adjust the channel types and quantities to adapt to various application scenarios; construction, installation, and commissioning can be carried out without power interruption;
Intelligent diagnosis
The system is equipped with a built-in partial discharge machine learning model.
Based on deep learning technology and trained with a massive library of normal and abnormal samples, the model undergoes continuous training and iterative optimization.
Leveraging edge computing, it enables fast and accurate local analysis as well as real-time fault warning.
