Research on error detection method of smart meters based on classification algorithm

The traditional error detection of electric energy meters uses the pulse method for inspection, which has a long cycle and low inspection efficiency when detecting low current errors. In order to improve the efficiency of intelligent electricity meter error detection, the article proposes a power testing method that integrates machine learning classification algorithms. This method preprocesses the detection data, using machine learning classification algorithm theory to learn the SVM (Support Vector Machine) model, using the training data to get the learned SVM model to process the data to be tested, improving the detection accuracy. In practical applications, this detection method has good stability and high detection efficiency. Partial project testing efficiency has increased by nearly 5 times and overall efficiency has increased by nearly 4 times. This method can be widely applied to electric energy meter manufacturers and measurement and testing institutions, better promote the development of electricity meter inspection technology, and achieve improved testing efficiency.