The data analysis and intelligent diagnosis function of the transformer DC bias online monitoring system transforms traditional manual experience judgment into data-driven scientific decision-making, achieving precise perception, accurate evaluation, and intelligent warning of DC bias problems. With the continuous development of artificial intelligence technology, the diagnostic capability of this system will be further enhanced, providing stronger technical support for the safe and stable operation of the power grid.
1、 Multi source data collection and fusion analysis
The transformer DC bias online monitoring system first collects multidimensional data such as DC current, vibration signal, noise level, oil temperature, etc. in real time through DC sensors, vibration sensors, noise sensors, temperature sensors, etc. deployed at the neutral point of the transformer. These data form the basis for analysis and diagnosis.
The system adopts data fusion technology to perform correlation analysis on monitoring data from different sources and types. For example, when an increase in DC current is detected, the system will synchronously analyze whether there is an enhancement of specific frequency components in the vibration signal, whether the noise level has increased, and whether there is an abnormal trend in temperature changes. This multi parameter joint analysis greatly improves the accuracy of fault identification and avoids the possibility of single parameter misjudgment.
2、 Feature Extraction and State Evaluation
After obtaining the raw data, the system extracts feature quantities through advanced signal processing algorithms. For vibration signals, methods such as wavelet analysis and empirical mode decomposition are used to extract characteristic frequency components related to DC bias; Perform spectral analysis on noise signals to identify the noise characteristics caused by iron core magnetostriction; For current signals, analyze the changes in their harmonic components.
Based on the extracted feature quantities, the system establishes a state evaluation model for transformers. By comparing real-time data with benchmark data under normal operating conditions, the system can quantitatively evaluate the severity of DC bias, usually using a graded warning mechanism: normal, alert, abnormal, dangerous, etc., providing intuitive status judgment for operation and maintenance personnel.
3、 Intelligent diagnosis and trend prediction
Intelligent diagnosis is the core function of the system. Based on machine learning and deep learning algorithms, the system can identify the development pattern of DC bias, determine the cause of bias (such as geomagnetic storms, single pole operation of DC transmission, etc.), and evaluate its impact on transformer insulation aging and mechanical structure stability.
The system also has strong trend prediction capabilities. By deeply mining and analyzing historical data, a time series prediction model is established to predict the development trend of DC bias and provide early warning of potential risks. For example, when it is predicted that geomagnetic activity will intensify in the future, the system can issue an early warning to give the operations department sufficient time to take preventive measures.
4、 Diagnostic decision-making and operational support
Based on the analysis and diagnosis results, the system can generate professional diagnostic reports, clearly indicating the location, severity, possible causes, and development trends of the problem, and proposing targeted treatment suggestions, such as adjusting the operation mode of DC transmission and putting in DC isolation devices.
In addition, the system will also link the diagnostic results with the equipment management system and maintenance management system, providing decision support for condition based maintenance and precise operation and maintenance, realizing the transition from "regular maintenance" to "condition based maintenance", greatly improving operation and maintenance efficiency, and reducing maintenance costs.
Conclusion