Data analysis and calibration of substation monitoring system based on Internet of Things (IoT)

The complex correlation of multi-source information of power equipment and the efficient validation of data information in the context of the Internet of Things (IoT) of electric power need to be studied urgently. The study applies density clustering to simplify the connection between multidimensional data and proposes a method for detecting anomalies in power equipment states based on interval set theory and density clustering. In addition, to ensure the accuracy of protection and measurement data for secondary equipment in substations, a dual verification system is established to sample secondary equipment data in the station area. The results of the related case study show that the anomaly detection method applying interval set clustering analysis can quickly and effectively detect the state anomalies of power equipment, which can be used as a decision-making basis for power grid troubleshooting. Based on the double calibration system of the guaranteed measurement data, it can realize the functions of power metering device error calibration, a secondary load test of the transformer, a voltage drop test of the secondary circuit of the voltage transformer, and so on.