Data monitoring method based on power big data

Abstract

The invention discloses a data monitoring method based on electric power big data. The method belongs to the technical field of electric power, and comprises the following steps: step 1, monitoring acquired data, acquiring data, acquiring total data of a single-phase meter and a related transformer area, importing the total data into hdfs, and monitoring data acquisition by adopting spark streaming + redis; step 2, performing data monitoring and real-time calculation, and calculation depends on scheduling daily electric quantity through a scheduling program; and recalculation: recalculating the daily electric quantity of all dates in the window by relying on a scheduling daily electric quantity recalculation program through a scheduling program. The clustering algorithm is adopted for continuous analysis, so that data quality is improved, the accuracy, timeliness, effectiveness and completeness of e data are guaranteed, a powerful guarantee is provided for integration and mining application of the data, the collection, calculation and uploading of the data are monitored, and the accuracy and timeliness of the data are guaranteed.

Description

technical field [0001] The invention relates to a data monitoring method, in particular to a data monitoring method based on electric power big data, and belongs to the field of electric power technology. Background technique [0002] With the in-depth development of distribution network service quality improvement work, higher requirements are put forward for the power distribution quality and power consumption management level of the station area. The three-phase lines in the station are divided into meters according to the phase, but most of the single-phase meters do not save the relevant phase information when they are archived. If the line phase information of the electric energy meter of the residential user is accurate, it will bring great convenience to the troubleshooting of faults, abnormal line loss and three-phase unbalance control in the station area. However, when wiring and installing electric energy meters in a large number of stations, the phase informatio...

AI Technical Summary

Problems solved by technology

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 installation phase information, due to the large amount of data in the entire network, it is impossible to manually count the timely rate, complete rate, and abnormal rate of data uploads such as various data collection, files, indicators, etc., plus the timeliness of these data The performance is very demanding, and the data needs to be monitored at different frequencies