Electricity metering equipment fault analysis method and device

Abstract

The invention discloses an electricity metering equipment fault analysis method and device. The method comprises steps that source data related to faults of electricity metering equipment are acquired; data cleaning of the source data is carried out; characteristic item selection of the source data after data cleaning is carried out; selected characteristic items are made to be sample data, and the sample data comprises training data, test data and prediction data; the training data and the test data are loaded into a machine learning model established based on the Xgboost algorithm, and the model is trained and tested; the prediction data is loaded into the trained model, and the electricity metering equipment fault analysis result is acquired . The method is advantaged in that accuracy of electricity metering equipment fault analysis can be improved, manpower resource cost is reduced, and efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of power metering, in particular to a fault analysis method and device for power metering equipment. Background technique [0002] In recent years, with the development of society, the demand for electricity for people's life and social production has been increasing, and the electricity data measured by electricity metering equipment has also shown explosive growth. The current situation of a large number of users and a large amount of metering data is a big challenge to the metering work of the power system. On the other hand, across the country, there are many faulty electric meters during the operation of hundreds of millions of electric meters. There are many types of faults in these meters, which will eventually affect the metering data and cause damage to the vital interests of users or power supply companies. Therefore, ensuring the safe and reliable operation of electricity metering equipment and ...

AI Technical Summary

Problems solved by technology

Although this method can find some measurement abnormalities to a certain extent, there are large errors through on-site verification.

The threshold value in this method is a range. How to set the threshold value as the most suitable value requires continuous on-site verification. Every time a threshold value is adjusted, it needs to be verified once. This requires a lot of human resources to support, and the time and efficiency are not very good. good guarantee

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