Clustering-based big data phase identification method

Abstract

The invention relates to a method for processing user phase information in a power supply court based on a K-means clustering algorithm to finish classification of user nodes in the power supply court. The method comprises the main steps of collecting voltage data of an intelligent electric meter running in an electric power system; processing the voltage data through PCA; setting an initial clustering center according to a data dimension reduction projection scattering point; and performing clustering grouping processing on the phase information to which a voltage belongs by utilizing the K-means algorithm, thereby realizing classification identification of user phases in the power supply court. The clustering-based big data phase identification method has the advantages that voltage information of the phase to which voltages belong is clustered by directly utilizing the K-means method; the user phase information can be accurately judged; the support is provided for network topology and three-phase imbalance management of a power distribution court; the number of users connected to each phase is reasonably adjusted and planned to stabilize the operation stability of the electric power system; extra hardware overhead does not need to be increased; a comparison threshold value does not need to be set; and the disadvantages of an existing phase identification technology are madeup for.

Description

technical field [0001] The invention relates to the field of classification of node phases in a user station area, in particular to a K-means clustering big data phase identification method, which belongs to the application field of distribution network electrical parameter measurement. Background technique [0002] In the distribution network operation and management of the power company, many issues related to the phase of the user's electric energy meter are involved, such as: the adjustment of the three-phase power imbalance in the transformer station area. When the management personnel find that there is a serious three-phase power imbalance in the power system When adjusting the user's phase, because you don't know the specific phase of the user, you don't know which users to choose for adjustment, and you can't evaluate the new customer's access to the network so that they can be installed on the most reasonable phase line. For another example, when conducting line lo...

AI Technical Summary

Problems solved by technology

The current phase identification technology has many deficiencies: although the installation phase identification of user nodes can be carried out through power line carrier communication technology, phase identification cannot be performed for station areas without power carrier; , the degree of automation needs to be improved, and it is necessary to build a secondary circuit, which requires a large investment and workload; in the process of user access processing, there is a lack of unified requirements for the color of the power line, and it is impossible to accurately identify the phase according to the color of the access user line

[0004] Although in the field of big data phase identification methods, the classification of user phases can be completed by linear correlation method, but this method will affect the classification results because of the selection of threshold; the clustering-based user phase identification method is an effective big data phase identification method, which can be used as a test standard for methods such as phase identification of power carrier

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