Power distribution area user phase identification method and system based on screened voltage data

Abstract

The invention relates to the technical field of low-voltage power distribution network automation, and discloses a power distribution area user phase identification method and system based on screened voltage data, so as to improve the accuracy of a user phase relationship. The method comprises the steps of: obtaining initial voltage time sequences corresponding to three-phase voltages of a transformer in a transformer area in the same period; screening out N time points with the maximum unbalance rate from the corresponding initial voltage time sequences according to a three-phase unbalance rate to construct a verification voltage time sequence corresponding to an A phase, a B phase and a C phase respectively; obtaining data of any to-be-verified user in the transformer area, and screening voltage data of the user at a time point where the verification voltage time sequence is located to construct a voltage time sequence of the user; and calculating dynamic time bending distances between the voltage time sequence of the user and the calibration voltage time sequences of the A phase, B phase and C phase of the transformer in the transformer area, and judging that a transformer phase corresponding to the calibration voltage time sequence with the minimum distance from the voltage time sequence of the user is the correct phase of the user.

Description

technical field [0001] The invention relates to the technical field of low-voltage distribution network automation, in particular to a method and system for identifying phases of distribution station users based on screening voltage data. Background technique [0002] The topological structure of the distribution network low-voltage sub-area is the basis of the operation and management of the sub-area. [0003] Due to the complex on-site wiring of low-voltage users and the large amount of data, the change of operation mode due to unbalanced load, the change of phase relationship, the lack of original data and poor quality, and the lack of effective means to check the manually entered data, resulting in computer systems. There are many errors in the topological data of the distribution network, and it is urgent to verify; to avoid adverse effects on the operation and management of the low-voltage station area due to inaccurate records of the relationship between the low-volta...

AI Technical Summary

Problems solved by technology

[0010] 1) Manual methods have problems such as high labor costs, low efficiency, and low accuracy

[0011] 2) The use of topology recognition instrument for phase difference verification needs to increase equipment investment, and there are problems such as "common high voltage crosstalk" and "common ground crosstalk", which affect the recognition accuracy, and the recognition workload is large, requiring a lot of manpower and material resources. less efficient

[0012] 3) The K-means algorithm is usually sensitive to noise and outliers, and the clustering center will be disturbed by abnormal data. Since the noise and outliers are far away from most objects, when it is assigned to a certain cluster, it will seriously distort the The mean value of the cluster affects the allocation of other objects to the cluster, making the deviation between the mean center and the actual center position too large, resulting in "distortion" of the cluster

When the algorithm calculates the gray relational degree, the weight of each variable is a fixed value, and there is a certain subjective judgment, which cannot accurately reflect the influence degree of the voltage characteristic value on the reference point under the conditions of light load, overload, etc.; Low recognition reliability

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