Data monitoring method based on power big data

A big data and data technology, applied in the field of data monitoring and data monitoring based on electric power big data, can solve the problems of unsaved phase information of electric energy meters of resident users, so as to improve efficiency and convenience, improve data quality, and save costs Effect

Pending Publication Date: 2020-02-11
CHINA SOUTH POWER GRID ELECTRIC POWER RES INST
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AI-Extracted 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 install...
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Method used

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 ...
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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.

Application Domain

Relational databasesResources +1

Technology Topic

Monitoring dataProgram calculation +12

Examples

  • Experimental program(1)

Example Embodiment

[0039] Example 1:
[0040] The working process of the data monitoring method based on power big data is as follows:
[0041] Step 1: Monitoring of collected data
[0042] (1), data collection, collect and import the single-phase meter and the total data of the relevant station area into hdfs,
[0043] (2) Use spark streaming+redis to monitor data collection, and monitor data collection at a frequency of every 15 minutes;
[0044] Step 2: Data Monitoring
[0045] (1) Real-time calculation, the scheduler relies on the scheduling day power calculation, the scheduler relies on the scheduling day power recalculation program, and the scheduler passes in the parameter time, calculates the daily power on all dates in the window and ends the calculation according to the scheduling statistics time Stored in the database, the stored procedure updates the latest statistical time data to the official database according to the type;
[0046] (2) Recalculation, relying on the scheduling daily power recalculation program through the scheduler to calculate the daily power on all dates in the window;
[0047] (3) Store the calculation results of real-time calculation and recalculation in the database and update it to the official database, use the correlation between the power data changes of the single-phase meter and the relevant station area total meter, and use the clustering algorithm for continuous analysis to infer Identify the phase connected to the single-phase meter;
[0048] (4), use spark+hbase to monitor the above calculation and storage process;
[0049] Step 3: Data upload monitoring
[0050] (1) Real-time feedback of data to the provincial and municipal metering systems. The main monitoring data for data upload monitoring are archives, indicators of municipal-level daily electricity and indicators of provincial-level daily electricity, and data problems are corrected in time according to the feedback from the provincial and municipal metering systems.
[0051]To sum up, in this embodiment, according to the data monitoring method based on electric power big data in this embodiment, the calculation process of the clustering algorithm is: record the power change of the single-phase meter at a certain moment and a certain phase of the total meter of the station area. At the same time, the power changes, and then compare the correlation between the value of the single-phase meter change and the data of the total meter change in the station area, and comprehensively analyze the correlation between the value of the single-phase meter change and the change data of the station area total meter. If the correlation becomes lower or higher and reaches the threshold value T, and the correlation of the data reaches the threshold value P at the same time, the phase connected to the single-phase table is inferred, and the time interval of the parameters passed in by the scheduler is from T-3 to T. -1, by collecting the instantaneous power data of the electric energy meter in the station area, and then through the edge computing capability in the metering terminal or the server computing capability in the metering automation system, analyze the instantaneous power data fluctuation of the electric energy meter of the residential user and the total meter of the station area The correlation between the power fluctuations of the relevant phases, so as to distinguish the phases connected to the single-phase electric energy meters installed by residential users, improve the efficiency and convenience of identifying the phases connected to the single-phase meters, and greatly save the cost of phase identification. Continuous analysis using clustering algorithm improves data quality, ensures the accuracy, timeliness, effectiveness and integrity of data, and provides a strong guarantee for data integration and mining applications. Accuracy and timeliness.

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Description & Claims & Application Information

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