Data monitoring method based on power big data

However, when wiring and installing electric energy meters in a large number of stations, the phase information of the residential users' electric energy meters is not saved, so that it is difficult to obtain the electric energy of residential users in the station area during troubleshooting, phase line loss control, and three-phase imbalance control. Table install...

In step one, the monitoring of collecting data is with the frequency monitoring data of every 15 minutes, and in step two, to the single-phase meter in database and the correlation of power data change of relevant station area general meter, adopt clustering algorithm Carry out continuous analysis, deduce and identify the phases connected to the single-phase meter. In step 2, the scheduler relies on the scheduling daily power recalculation program, and at the same time, the scheduler passes in the parameter time to calculate the daily power of all days in the window and calculate At the end, it is stored in the database according to the scheduled statistical time, and the storage process updates the latest data of the statistical time to the official database according to the type. In step 3, the data uploaded and monitored mainly monitors the files, indicators, city-level daily electricity, and indicators provinces. In step 3, according to the feedback from the provincial and municipal metering systems, data problems are corrected in a timely manner, by collecting the instantaneous power data of the electric energy meters in the station area, and then through the edge computing capability in the metering terminal or the server in the metering automation system Computing ability, analyze the correlation between the instantaneous power data fluctuation of the residential user's electric energy meter and the relevant phase power fluctuation of the general meter in the station area, so as to distinguish the phase connected to the single-phase electric energy meter installed by the residential user, and improve the identification of the single-phase electric energy meter. The phase efficiency and convenience of the mount can greatly save the cost of phase identification. At the same time, the use of clustering algorithms for continuous analysis improves the data quality, ensures the accuracy, timeliness, effectiveness and completeness of the data, and provides powerful data integration and mining applications. Guarantee, monitor the collection, calculation and upload of ...