Transformer area line loss abnormity associated user accurate positioning method based on data mining

Abstract

The invention discloses a transformer area line loss abnormity associated user accurate positioning method based on data mining. The method comprises the steps: acquiring a given abnormal transformerarea line loss rate; carrying out the K-means clustering; establishing a transformer area line loss rate standard library and an abnormal library; determining an abnormal time period; preprocessing the power consumption data to obtain user electric quantity with research significance; calculating pearson coefficients of the electric quantity and the line loss rate of each user in the abnormal timeperiod respectively; carrying out preliminary screening by utilizing a set threshold value to obtain a user electric quantity set which is greatly associated with the line loss abnormity; respectively calculating the improved Euclidean distance between each user electric quantity curve and the line loss rate curve in the set; and based on the similarity measurement of the weighted Pearson coefficient and the Euclidean distance, calculating a weight coefficient of the Pearson coefficient and the Euclidean distance, and accurately positioning all abnormal users. According to the method, the relevance between the user electric quantity and the line loss rate of the transformer area in a single scene is considered, and the historical data analysis of the specific transformer area is combined,so that the rapidity and accuracy of accurate positioning are improved.

Description

technical field [0001] The invention belongs to the technical field of accurately positioning anomalies under big data, and in particular relates to a method for accurately locating users associated with line loss anomalies in a station area based on data mining. Background technique [0002] With the implementation of national energy resource efficient development and utilization strategy, energy conservation has gradually become an important means to alleviate energy supply contradictions, and the power industry is one of the important areas of energy conservation work. As an important support for economic and social development, electricity is one of the important tasks of power supply enterprises to reduce the loss and loss of electric energy in the process of transmission, distribution and sales. [0003] The existing user location method associated with line loss anomalies in the station area has some shortcomings in terms of specific calculation examples and functiona...

AI Technical Summary

Problems solved by technology

First, only considering the use of the Pearson coefficient algorithm to determine the degree of correlation between user power fluctuations and line loss rate changes, without specific analysis of the shape similarity between the two curves of abnormal user power and line loss rate; second, due to the amount of line loss data Huge, it is necessary to analyze the relationship between line loss fluctuations and power fluctuations through data mining algorithms, accurately locate abnormal users, and carry out targeted management of line loss. Positioning and checking one by one, ignoring the analysis and mining of big data in the Taiwan area, not only increased the amount of calculation, but also lacked accuracy

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