Research on Self-Learning and Self-Correcting Technology of Sampling Deviation of Substation Secondary Equipment

With the increasing demand for stable power supply in economic development, the reliability requirements of secondary equipment in substation are also increasingly strengthened. At present, the secondary equipment involved in the collection of primary equipment operation status mainly includes the merging unit and the measurement and control device, whose function is mainly to collect the voltage, current and position signals of the primary transformer. Substations may be built in areas with high temperature, low temperature, high humidity, high altitude and other harsh climate, especially the merging units, in some substations are installed in outdoor cabinets, the operating environment is harsh. Climate change, especially temperature change, often has a bad effect on the sampling accuracy, which leads to the wrong action of the follow-up protection device and threatens the safe operation of the substation. The technology proposed in this paper can complete the statistics of the influence data of environmental factors on the sampling accuracy through secondary equipment self-learning, and then form the accuracy deviation curve for different sampling channels through big data analysis and fitting. Finally, the accuracy deviation curve is applied to the substation secondary equipment in mass production. The sampling accuracy can be automatically corrected by sensing the changes of the environment in real time and comparing with the accuracy deviation curve during the field operation. This technology improves the sampling accuracy of the substation secondary equipment and enhances the stability of the substation operation.