Research on Abnormity Detection based on Big Data Analysis of Smart Meter

Abstract

There are over five hundred million smart meters in China. The current standard for the use of smart meters is physical inspection of meter dismantling within 8 years. The method leads to many issues including high cost of testing, low sampling rate, unknown meter status huge waste of resources etc. Searching for non- dismantling meter detection solution is necessary. Although the smart grid can be managed much better with the increasing use of smart meters, the current standard brings many issues. To solve the problems like a huge waste of resources, detecting inaccurate smart meters and targeting them for replacement must be done. Based on the big data analysis of smart meters, abnormity can be predicted and diagnosed. For this purpose, the method is based on Long Short-Term Memory (LSTM) and a modified Convolutional Neural Network (CNN) to predict electricity usage patterns based on historical data. In this process, LSTM is used to fit the trend prediction of smart meters, and recurrence plot is used to detect the abnormality of smart meter. Both LSTM and recurrence plot method is the first time to be used in smart meter detection. In actual research, many methods including Elastic Net, GBR, LSTM and etc. are used to predict the trend of smart meters. Through the best method LSTM, the accurate rate of the trend prediction of smart meters can arrive at about 96%. Similarly many methods are used to detect the abnormality of smart meters. In single-input modeling, there are sequence-input and matrix-input methods. In dual-input modeling, there are TS-RP CNN, VGG+BiLSTM, ResNet50+1D-CNN and ResNet50+BiLSTM etc. Eventually based on the most successful method recurrence plot, the abnormity testing and failure recognition can be got at 82% roughly. This is the breakthrough in the electricity power domain. With the success of the solution, the service time of a normal meter can be prolonged by abnormity detection. This will lead to saving a lot of resources on smart meter applications.