Program overview:

The three-dimensional visualization system of power transformer fault prediction and health management (PHM) is mainly composed of three-dimensional visualization display module, video acquisition and sensor acquisition module, data analysis and state evaluation module, fault prediction and health management module. It comprehensively analyzes the equipment daily operation information, equipment maintenance information, equipment management and control information, etc Make comprehensive evaluation and provide decision-making maintenance suggestions for users.

Background and advantages:

At present, the fault mechanism of power transformer equipment is complex and the maintenance cost is high, which has a great impact on the safe and stable operation of power system. In the case of complete data, it has a significant positive effect on the safe and stable operation of power system to obtain the health indicators of transformer operation state and carry out appropriate state maintenance and preventive maintenance.

The system manages and analyzes the management and control information, product operation information and daily maintenance data in the process of equipment transportation and operation, completes the comprehensive evaluation of power transformer equipment status, and provides users with operation suggestions, equipment shutdown and other decision-making suggestions. The system puts forward a kind of decision-making expert diagnosis function, and uses the power transformer equipment Based on operation, fault prediction and health management, combined with three-dimensional visualization technology, data mining technology and cloud computing services, the system realizes the power transformer management from passive maintenance to active maintenance and grasps the global information.

• The technical products are reliable and stable
• Good human-computer interaction interface is easy to read and operate
• Three dimensional visualization, clear and intuitive
• Big data analysis and cloud computing improve the active maintenance and fault prediction of equipment
• The system has alarm data interface, which can be connected to the government enterprise platform

Solution:

The system mainly consists of data acquisition, data processing, 3D model display, state evaluation, fault diagnosis, fault prediction, decision service and health management. The detection data which can reflect the actual health condition of power transformer is obtained by means of digital sensor exchange. The required data set is provided for PHM driven by basic data. The current state and degradation degree of power transformer are comprehensively evaluated through data analysis, and the equipment fault is predicted for maintenance personnel to evaluate whether it needs to be maintained in advance. Finally, the conclusion of each stage is integrated into a complete three-dimensional visualization system of power transformer fault prediction and health management.

1. data acquisition

Video image data, temperature data, vibration data and sound data of power transformer equipment during operation are collected by using video image technology and optical fiber sensing technology.

2. data processing

The collected image data, temperature data, vibration data and sound data are analyzed by means of big data and machine learning. According to the interaction between the external environment and the internal structure data, the method based on artificial intelligence and statistics is used to establish a multi-dimensional data comprehensive model for multi-dimensional analysis.

3. 3D visualization

Using 3D modeling, the 3D model of transformer is constructed, and the collected data and analysis results are displayed in the way of 3D display of various countries.

4. Condition assessment

According to the conclusion of the analysis data, the status of the current transformer is comprehensively analyzed to provide operation and maintenance suggestions for the operation and maintenance personnel.

5. Fault diagnosis and prediction

By means of big data analysis and machine learning, multi-dimensional data analysis is carried out on the data of power transformer, and the fault is scientifically predicted, and the fault is analyzed, and the diagnosis opinions are given.

6. Decision making service and health management

Based on the analysis of a large number of data and the experience of some senior personnel in the industry, the fault mechanism of power transformer is analyzed in depth, and the remaining life is scientifically analyzed, and the decision-making opinions are given.