With the emergence and development of the concept of smart grids, all aspects of power systems are facing the demand for intelligent upgrading. As a key component in the grid, the intelligent transformation of transformers is of great significance for ensuring the safe, stable, and efficient operation of the grid. In the past, traditional
transformers often required manual regular inspections and maintenance during operation, and it was difficult to obtain real-time equipment status information or issue timely warnings before faults occurred. However, under the background of smart grids, the intelligent upgrading of transformers provides an effective solution to these problems.
The installation of various intelligent sensors on transformers is the foundation for achieving intelligence. For example, temperature sensors can monitor the temperature changes of key parts such as transformer windings and oil in real time. Pressure sensors can detect the internal pressure of the oil tank, which is crucial for determining whether there are abnormal pressures caused by internal faults such as short circuits and discharges. In addition, vibration sensors can analyze potential problems such as core loosening and winding deformation by monitoring the vibration of the transformer. These sensors convert the collected physical quantities into electrical signals, providing the original data source for subsequent data analysis.
Communication modules enable transformers to exchange information with the outside world. Through wired or wireless communication technologies (such as optical fiber communication, ZigBee, 4G/5G, etc.), transformers can transmit the data collected by intelligent sensors to the grid monitoring center. Meanwhile, they can also receive control instructions from the monitoring center to realize remote monitoring and operation. For example, when the monitoring center receives the alarm information that the transformer oil temperature is too high, it can remotely adjust the operating parameters of the transformer, such as reducing the load, to ensure the safe operation of the equipment.
With the help of intelligent sensors and communication modules, transformers have the ability to self-monitor. They can comprehensively monitor their own operating status in real time, including electrical parameters (such as voltage, current, power factor, etc.) and non-electrical parameters (such as temperature, pressure, vibration, etc.). Through preset thresholds and algorithms, once the monitored data exceeds the normal range, the transformer can immediately and automatically send out warning information to notify the operation and maintenance personnel to conduct inspections and handle the situation. For example, when the winding temperature exceeds the set warning temperature, the monitoring system of the transformer will send an alarm signal to the monitoring center in a timely manner and provide detailed temperature data and location information so that the operation and maintenance personnel can quickly locate the fault point.
Based on a large amount of monitoring data and advanced data analysis algorithms, the intelligent system of transformers can realize the fault diagnosis function. By learning and analyzing historical data and establishing fault models, when the real-time monitoring data matches the fault models, it can determine the possible fault types and locations. For example, by using the dissolved gas analysis (DGA) technology in oil combined with the data of intelligent sensors, if the contents of characteristic gases such as hydrogen and acetylene are abnormally increased, the system can diagnose whether there are partial discharge or overheating faults inside the
transformer and further analyze the severity of the faults to provide a basis for formulating maintenance plans.
Using big data analysis and artificial intelligence algorithms, transformers can predict future load conditions. By comprehensively analyzing the load data in the past period (such as daily load curves, weekly load curves, seasonal load changes, etc.) and related external factors (such as weather, day type, economic activities, etc.), a load forecasting model is established. The prediction results can help the grid dispatching department adjust the operation strategy in advance, reasonably allocate power resources, avoid the overload operation of transformers, and improve the economic efficiency and reliability of grid operation. For example, before the summer peak electricity consumption period arrives, based on historical data and weather forecast information, if it is predicted that the load of transformers in a certain area will increase significantly, the grid dispatching department can arrange other power sources to provide support or transfer some loads in advance to ensure that the transformers operate within a safe range.
- Real-time Monitoring and Remote Operation
The intelligence of transformers enables grid operation and maintenance personnel to monitor the operating status of transformers in real time. No matter where they are, as long as they are connected to the monitoring center through the network, they can grasp the various parameters and operating conditions of the equipment at any time. Moreover, when necessary, they can remotely operate the transformers, such as adjusting taps and switching operation modes, which greatly improves the convenience and flexibility of operation management.
- Data Analysis and Decision Support
A large amount of monitoring data of transformers provides rich information resources for grid operation management. Through in-depth analysis of these data, the laws of equipment operation and potential problems can be explored, providing powerful data support for formulating scientific and reasonable operation and maintenance strategies, equipment renewal plans, and grid planning. For example, according to the aging trend and failure rate prediction results of transformers, the maintenance time and content of equipment can be reasonably arranged to avoid excessive or insufficient maintenance and improve the utilization rate of equipment and the operation efficiency of the grid.
- Fault Warning and Rapid Handling
The self-monitoring and fault diagnosis functions of transformers can detect potential faults in advance and issue warning information in a timely manner. This enables operation and maintenance personnel to have enough time to take measures, such as arranging power outage maintenance and deploying standby equipment, to avoid the further expansion of faults, thereby effectively reducing the occurrence of power outage accidents and improving the power supply reliability of the grid. For example, before a serious fault occurs in a transformer, the fault diagnosis system can detect and replace the faulty components in time, preventing large-scale power outages caused by transformer faults and ensuring the normal power consumption of users.
- Redundancy Configuration and Intelligent Switching
In smart grids, multiple transformers can achieve intelligent collaborative work. When a certain transformer fails or is overloaded, other normally operating transformers can automatically adjust the load distribution according to the system's instructions, realizing the functions of redundancy backup and intelligent switching. This intelligent operation mode greatly improves the fault tolerance ability of the grid in the face of equipment failures and further enhances the reliability of the grid.
- Load Forecasting and Resource Allocation
The load forecasting function of transformers provides an important basis for the energy optimization allocation of the grid. By accurately predicting the load changes of transformers in various regions, the grid dispatching department can plan the allocation of power resources in advance, reasonably distribute power generation resources to various load centers, and avoid the waste and unreasonable allocation of power resources. For example, according to the load prediction results of different periods and regions, the power generation plans of different power sources such as thermal power, hydropower, and wind power can be arranged to balance power generation and power consumption and improve energy utilization efficiency.
- Distributed Energy Integration and Coordination
With the wide application of distributed energy (such as solar energy and wind energy) in smart grids, transformers need to have better compatibility and coordination capabilities. Intelligent transformers can monitor the integration of distributed energy in real time and make intelligent adjustments according to its power generation power and grid load requirements. For example, when the power generation of distributed energy is excessive, the transformer can store or transmit the surplus electric energy to other areas in need; when the power generation of distributed energy is insufficient, the transformer can obtain electric energy from the grid for supplementation to ensure the stable operation of the entire grid, promote the effective utilization of distributed energy, and optimize the energy structure.
In this project, the transformers in multiple substations in the core area of the city were upgraded and transformed intelligently. Each transformer was equipped with high-precision intelligent sensors for temperature, pressure, vibration, etc., and integrated with 5G communication modules. Through the establishment of a unified grid monitoring platform, real-time centralized monitoring of all transformers was achieved. During the operation process, the self-monitoring system of the transformers detected potential fault hazards in a timely manner on many occasions, such as abnormal increases in oil temperature caused by poor heat dissipation and slight winding deformations caused by external construction vibrations. Through the fault diagnosis function, the fault types and locations were accurately determined, and maintenance personnel were arranged to handle the problems in a timely manner, avoiding the occurrence of faults and effectively ensuring the power supply reliability in the core area of the city. Meanwhile, by using the load forecasting function and combining factors such as the city's electricity consumption patterns and arrangements for large-scale activities, the operation modes and load distributions of the transformers were reasonably adjusted, improving energy utilization efficiency and reducing grid losses.
In this industrial park, a smart micro-grid system with solar energy and wind energy as the main distributed energy sources was constructed, and the transformers in it adopted intelligent designs. The transformers were closely connected with the energy management system in the micro-grid through intelligent sensors, monitoring the power generation power of distributed energy, the electricity consumption loads of enterprises in the park, and their own operating states in real time. When solar energy generation was sufficient and the park load was low, the transformers stored the surplus electric energy in energy storage devices; when solar energy generation stopped at night and the park load was high, the transformers obtained electric energy from energy storage devices or the external grid for supplementation. During an extreme weather event that caused a failure of the external grid, the transformers, relying on their intelligent switching functions, quickly adjusted their operation modes, isolated and protected the distributed energy and important loads in the park, and used the electric energy in the energy storage devices to maintain the operation of some key production equipment, ensuring the basic production needs of enterprises in the park and demonstrating the important role of transformer intelligence in improving the reliability of micro-grids and dealing with emergencies.
In conclusion, the intelligent upgrading of transformers plays an irreplaceable important role in the construction of smart grids. By realizing intelligent functions such as self-monitoring, fault diagnosis, and load forecasting, it has significantly improved the grid operation management level, reliability, and energy optimization allocation ability. With the continuous development of smart grid technologies, the intelligent applications of transformers will continue to be improved and expanded, laying a solid foundation for building a more intelligent, efficient, and reliable power system.
