Identification Method of Wrong Wiring of Smart Watt-Hour Meter Using CNN Feature Extraction and Light GBM Algorithm

As intelligent watt-hour meters become more widespread in power systems, wiring errors have become more prevalent, significantly affecting the accuracy of energy measurement and the stability of the power system. This study proposes a method to detect incorrect wiring in intelligent watt-hour meters by leveraging feature extraction through convolutional neural networks (CNN) combined with the Light Gradient Boosting Machine (Light GBM) algorithm. Initially, we designed an enhanced CNN model tailored to automatically derive meaningful features from the wiring images of watt-hour meters. The CNN encompasses multiple convolution layers, pooling layers, ReLU (Linear rectification function), and a fully connected layer. During the training phase, the model utilizes backpropagation alongside a cross-entropy loss function to effectively identify and extract critical features from wiring images. Following this, Principal Component Analysis (PCA) is applied to streamline the extracted features of high dimensionality. This process serves a dual purpose: it reduces the computational load and decreases the likelihood of overfitting. Then, a proficient Light GBM classifier is developed using the reduced-dimension features as inputs to assess the wiring status of the watt-hour meter. Experimental outcomes indicate that our approach distinguishes between correct and incorrect wirings, achieving an accuracy rate of 98.5%, a recall rate of 98.0%, and an F1 score of 98.3%. Compared with traditional image processing methods and other deep learning models, this method significantly improves the accuracy and efficiency of identifying the wrong wiring of watt-hour meters. This paper proposes the identification method of miswiring of smart watt-hour meter based on CNN feature extraction and Light GBM classification, which provides an efficient and reliable power system operation and maintenance solution.