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Land Transport

Power Transformer PHM 3D Visualization Monitoring System

 

Solution Background

The current fault mechanism of power transformer equipment is complex and maintenance costs are high, which significantly impacts the safe and stable operation of power systems. With complete data, obtaining health indicators of transformer operating status and performing appropriate condition-based maintenance and preventive maintenance has a significantly positive effect on the safe and stable operation of power systems.

Solution Introduction

Solution Overview

The Power Transformer Fault Prediction and Health Management (PHM) 3D Visualization System is mainly composed of a 3D visualization display module, video and sensor acquisition module, data analysis and status assessment module, and fault prediction and health management module. It performs comprehensive analysis on data such as daily equipment operation information, equipment maintenance information, and equipment control information, conducts a comprehensive evaluation of power transformer equipment, and provides users with decisive maintenance recommendations.

This system manages and analyzes various data sources such as control information, product operation information, and daily maintenance data during equipment transportation and operation, completes a comprehensive status evaluation of power transformer equipment, and provides users with operational suggestions, equipment shutdown, and other decisive recommendations. This system proposes a decisive expert diagnostic function, and based on power transformer equipment operation, fault prediction, and health management, combined with 3D visualization technology, data mining technology, and cloud computing services, starting from enhancing the full life cycle value of power transformer equipment, it realizes a system for power transformer management from passive maintenance to proactive maintenance and grasping global information.

Applicable Scenarios

  • Large Hub Substations: Suitable for main transformers in hub substations of 500kV voltage level and above, ensuring the safe operation of core grid equipment through precise monitoring and visual management.
  • High-Load Operating Transformers: For transformers operating during peak power consumption or under long-term high load, real-time monitoring of equipment status and early prediction of potential fault risks.
  • Old Transformers: Assessing the health status of transformers with long service life and declining performance, assisting in formulating scientific decommissioning and refurbishment plans.
  • Smart Substations: Interfacing with the overall intelligent system of smart substations to achieve transformer data sharing and collaborative management.

Core Functions

Function Module Description
Data Acquisition Utilizing video imaging technology and fiber optic sensing technology to collect video image data, temperature data, vibration data, and sound data of power transformer equipment during operation.
Data Processing The collected image data, temperature data, vibration data, and sound data are analyzed using various methods, including big data and machine learning. Based on the premise of mutual influence between external environmental effects and internal structural data, artificial intelligence and statistical methods are employed to build comprehensive multi-dimensional analysis models for various types of data.
3D Visualization Display Using 3D modeling to build 3D models of transformers, and displaying the collected data and analysis results using global 3D visualization methods.
Status Assessment Based on the conclusions drawn from analyzed data, a comprehensive analysis of the current transformer status is performed, providing maintenance suggestions for operations and maintenance personnel.
Fault Diagnosis and Prediction Utilizing big data analysis and machine learning methods to perform multi-dimensional data analysis on power transformer data, scientifically predict its faults, analyze occurring faults, and provide diagnostic opinions.
Decision Service and Health Management Based on the analysis of large amounts of data and the experience of industry veterans, a deep analysis of power transformer fault mechanisms is conducted, scientific analysis of remaining life and other factors is performed, and decisive opinions are provided.

Technical Advantages

  • Digital Twin Driven: Based on digital twin technology, it achieves precise mapping between virtual models and physical equipment, truly restoring equipment operating status.
  • Multi-Source Data Fusion: Integrates various types of data, including electrical, mechanical, and chemical, to comprehensively reflect equipment health status and improve the accuracy of fault prediction.
  • Visualized Decision-Making: The 3D visualization interface reduces the difficulty of data comprehension, enabling operations and maintenance personnel to grasp equipment status more quickly and accurately, and improving decision-making efficiency.
  • Predictive Maintenance: Through PHM technology, potential faults are detected in advance, transforming passive maintenance into proactive maintenance, reducing operation and maintenance costs and power outage losses.
  • High Compatibility and Scalability: Supports various communication protocols and sensor access, and function modules can be extended as needed to adapt to different application scenarios.

Application Cases

Case Study 1: A Certain UHV Substation

  • Project Background: The main transformers in the substation are highly valuable and difficult to maintain, requiring real-time monitoring of equipment health status to avoid major faults.
  • Solution Implementation: Deployed a power transformer PHM 3D visualization monitoring system, integrating data from 200+ monitoring points such as temperature, vibration, and oil chromatogram.
  • Application Effect: The system successfully predicted potential local overheating of transformer windings, arranged maintenance in advance to prevent faults; operation and maintenance efficiency increased by 40%, and maintenance costs reduced by 30%.
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Solution Introduction

Application Cases

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