Partial discharge of transformers refers to the phenomenon of local breakdown of insulation materials inside equipment due to uneven electric fields. If this minor fault is not detected in a timely manner, it will gradually erode the insulation structure and ultimately lead to equipment failure. Traditional monitoring methods require power interruption for offline detection, which not only affects normal power supply but also cannot continuously track partial discharge dynamics, often missing the early stages of faults. The partial discharge online monitoring system can continuously collect partial discharge signals 24 hours a day by deploying high-precision sensors inside and around the transformer, capturing key data such as pulse current, ultrasound, and ultra-high frequency in real time, and transmitting the data to the backend analysis platform through a dedicated communication network. This "real-time monitoring+data transmission" mode breaks the time and space limitations of traditional maintenance and achieves all-weather "close monitoring" of transformer operation status.

The core advantage of the system is more reflected in its intelligent analysis and warning capabilities. The backend platform is equipped with advanced artificial intelligence algorithms, which can deeply process the massive data collected, automatically identify partial discharge signals and interference signals, and accurately determine the location, strength, and development trend of partial discharge. When the partial discharge value exceeds the safety threshold, the system will immediately trigger a multi-level warning mechanism, send alarm information to operation and maintenance personnel through SMS, platform pop ups, and other means, and generate a fault analysis report to provide accurate guidance for maintenance work. For example, in the practical application of a certain substation, the system detected abnormal partial discharge in a main transformer 72 hours in advance. The operation and maintenance personnel carried out targeted maintenance in a timely manner based on the system positioning, successfully avoiding an equipment accident that may cause regional power outages, significantly reducing the cost of fault handling and power outage losses.

In addition, the transformer partial discharge online monitoring system has also promoted the transformation of power grid operation and maintenance towards "preventive management". Through long-term accumulation of partial discharge data, the system can establish a transformer health status assessment model, predict the remaining life of equipment, help operation and maintenance personnel formulate scientific maintenance plans, avoid blind power outage detection, and greatly improve the reliability of power supply in the power grid. According to statistics, in the areas where the system is deployed, the average occurrence rate of transformer failures has decreased by 40%, and the duration of power outages has been reduced by 60%, providing stable power supply for industrial production and residential life.

In the stable operation of the power system, the safety status of transformers, as the core equipment, directly determines the reliability of the power grid. However, the traditional transformer maintenance mode has obvious shortcomings, relying heavily on regular power outage detection, which not only has low efficiency, but also makes it difficult to capture the hidden dangers of partial discharge (referred to as "partial discharge") faults inside the equipment in real time. Once the partial discharge problem worsens, it can easily lead to serious accidents such as transformer burnout and power grid paralysis. In this context, the research and application of transformer partial discharge online monitoring system has become a key breakthrough in safeguarding the safety of the power grid, bringing revolutionary changes to the operation and maintenance of the power system.