Power Big Data Analysis And Smart Meter Anomaly Detection

With the development of technologies such as communication technology,sensor technology,and intelligent computing,smart grids around the world are also accelerating construction.In the context of smart grids,electricity and big data are growing exponentially,and the demand for smart meters will continue to increase,and the overall market will continue to grow.Large-scale global smart grid construction will bring a broader market demand for the smart meter industry.Among them,the smart meter is a mandatory verification device,and the fixed use period is generally 5-8 years,and it needs to be replaced when it expires.However,most smart meters are still in good shape after 8 years of use.Therefore,the analysis of the use of electricity big data,the abnormal diagnosis of smart meters is extremely important for saving financial and human resources.In this paper,the deep-study technology is used to analyze and research the smart meter data.It is hoped that the user’s electricity data law will be discovered and the trend forecasting and anomaly detection of the smart meter will be carried out to find out the faulty smart meter and replace it.This paper mainly completes the following three aspects:1、Analyze the collected smart meter data,introduce the generation,importance,and characteristics of electricity consumption big data,and analyze the user’s electricity consumption habits and laws.2、This study assumes that the total meter of the smart meter is not abnormal under careful maintenance.In this case,the double-layer long and short-term memory network is used to determine the residential area based on the historical data of the sub-meter electricity and the total meter electricity The error trend of smart meters is predicted to determine whether there are abnormal smart meters in residential areas.3、If the presence of an abnormal smart meter in the residential area is detected in 2,it will enter the classification task.In this study,a convolutional neural network algorithm is used to identify and classify each sub-meter in the residential area.In order to better detect abnormal smart meters,this study converted the sequence data into Recurrence Plots(RP)and used it as the input of the convolutional neural network model.Through comparative analysis,it was found that the traditional time series data input Higher accuracy.Finally,compared with the classical model,the method proposed in this paper has the highest accuracy.In this paper,the innovation of the method is to convert the meter data into RP graph as the input of CNN classification model.In application,it combines deep learning with smart meter anomaly detection.Compared with previous anomaly detection methods,the accuracy rate has been greatly improved.The proposed method has practical significance for the development of smart grid and the popularization of smart meters.