Medium-voltage distribution network user electricity consumption abnormity diagnosis method based on machine learning

Abstract

The invention discloses a medium-voltage distribution network user electricity consumption abnormity diagnosis method based on machine learning. The method comprises the steps of generating a potential electricity consumption abnormity user set based on acquired user name data; on the basis of the potential abnormal power utilization user set, acquiring forward active power, voltage and three-phase current data of the industry to which the potential abnormal power utilization user set belongs and near two cycles; carrying out missing value preprocessing on the obtained data; calculating and adding 5 eigenvalues into historical power consumption data anomaly four-level labels of all the users to form a sample set; dividing the sample set, training an artificial intelligence model, and testing a model effect and model tuning; carrying out model training and evaluation by adopting a random forest in a machine learning integration algorithm; and carrying out batch marking processing by using the trained model. The method is simple in calculation, and can help an operator to find and adjust an abnormal line loss line in time.

Description

technical field [0001] The invention belongs to the field of diagnosing abnormal power consumption of medium-voltage distribution network users, and in particular relates to a method for diagnosing abnormal power consumption of medium-voltage distribution network users based on machine learning. Background technique [0002] The abnormal power consumption of the medium-voltage distribution network refers to abnormal phenomena such as component failure, abnormal data collection, and abnormal power consumption by criminals. The abnormal cases of electricity users traced out manually mainly rely on the on-site investigation of grassroots employees. Due to factors such as large number of users, complicated equipment, long time-consuming, various abnormal electricity usage methods, and difficulty in investigation, it is difficult to be comprehensive and targeted. Evaluating the power consumption status of each user in a timely manner will lead to business pain points such as high...

AI Technical Summary

Problems solved by technology

The abnormal cases of electricity users found out by manual investigation mainly rely on the on-site investigation of grassroots employees. Due to factors such as large number of users, complicated equipment, long time consumption, various abnormal electricity usage methods, and difficulty in investigation, it is difficult to be comprehensive and targeted. Evaluate the power consumption status of each user in a timely manner, which will lead to business pain points such as high line loss, high calculation error of medium voltage line loss rate, and low accuracy of reliability analysis, which seriously restricts the construction process of a first-class distribution network

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