Fault Prediction Method of Watt-Hour Meter Based on Decision Tree Algorithm

Their components are complex and the fault types are diverse. Most maintenance personnel do not have the ability to repair all types of faults. Therefore, when smart meters fail, they can accurately predict the guide the operation and maintenance personnel to dispatch, It will effectively reduce the human and resource costs and maintenance. The demand for electricity. The current situation requires and electricity to meet the growing. In this case, it is very important to predict the possible faults in the power system, so that these faults can be avoided at any cost. This can be achieved by using various technologies, such as load forecasting, voltage stress testing, etc. However, an uncommon technology is the watt-hour meter fault prediction method (WattFPA). The fault prediction technology of intelligent watt-hour meter is studied. Firstly, a deep neural network structure that can extract the depth attribute of fault data is designed, and the model optimization and parameter debugging methods are studied, and the optimal model is constructed; Secondly, in multi-class in intelligent meter fault prediction, a cost-sensitive multi-class XGBoost model is designed, and the relevant parameter debugging method is analyzed, and the model effect is verified on the test data.