Fault Detection of Electricity Meters Based on PCA-MLP Neural Network

The mechanism and components of electric meter faults have not been thoroughly developed in the market, and there are still no mature intelligent detection methods available. Fault analysis still requires experienced technicians who are familiar with the technical principles of electric meters to open the cover for inspection. Furthermore, there is no unified technical standard in the industry for reference when it comes to analysis methods. To address this issue, a fault detection method for electric meters based on PCA-MLP neural networks is proposed. This method obtains the power data from the electric meter and processes and analyzes this data to generate power images. These power images are then compared with predefined fault patterns to determine whether the electric meter is faulty. This method enables non-destructive testing without opening the cover, thereby reducing maintenance costs and time. By using power images as features and processing fault data with MLP, this method can accurately identify the type of electric meter fault, improving the accuracy and efficiency of fault detection. Experimental results on a real data-set have verified the effectiveness and stability of this method.