Operational Status Monitoring and Fault Diagnosis System of Transformer Equipment

Abstract

In order for valuable distribution transformer data to provide a potential solution for monitoring abnormal conditions, the authors propose a data-driven abnormal state monitoring data acquisition algorithm for distribution network transformers. The algorithm can alert operators and maintenance personnel of abnormal conditions in a timely manner. In the proposed algorithm, the Spearman rank correlation coefficient is used to display the correlation between phase currents, and its $t$ statistic is used to determine whether there is an abnormality in the determined data collection based on hypothesis testing. Finally, the effectiveness of the proposed algorithm is verified by using the actual data collected from the power grid, and the characteristics of normal and abnormal conditions are analyzed separately. Sensitivity analyses were performed for different significance levels and sampling rates to consider their impact on monitoring results. The application results show that the power grid recovered a total of 12.98 million yuan from 136 households with a power consumption of 17.57 GWh, proving the practicability of the algorithm. Conclusion. The application in the actual power system is given, and the feasibility of the algorithm is proved.