基于最大似然法和决策树的智能电能表计量误差检测方法

A measurement error detection method for smart electricity meter based on maximum likelihood method and decision tree

电能计量设备校验不及时可能会出现一些误差,研究计量误差检测方法具有重要意义,为此设计基于最大似然法和决策树的智能电能表计量误差检测方法。对智能电能表计量原始数据实施数据清洗与降维;基于最大似然法,计算清洗与降维后异常特征数据项的可分离性,当计算获得的可分离性越高,说明该数据项对于异常有着越明显的分类效果,选择可分离性较高的数据项,输入至基于XGBoost算法构建精准研判函数中,通过网格搜索法实施超参数搜索,结合算法自适应能力与自学习能力,确认计量误差,提升对于电能表计量误差的精准研判能力,实现智能电能表计量误差检测。测试结果表明,该方法在多种子区中都取得了较高的检测准确率,整体具有较好的稳定性和泛化能力。

The untimely calibration of electric energy metering equipment may result in some errors.It is of great significance to study measurement error detection methods.Therefore,a measurement error detection method for smart electricity meter based on maximum likelihood method and decision tree is designed.Data cleaning and dimensionality reduction are implemented on the raw measurement data of smart electricity meters.Based on the maximum likelihood method,the separability of abnormal feature data items after cleaning and dimensionality reduction is calculated.The higher the separability obtained from the calculation,the more obvious the classification effect of the data item on anomalies.The data items with higher separability are selected and input into the XGBoost algorithm to construct an accurate judgment function.The hyperparameter search is implemented through grid search method,combined with the adaptive ability and self-learning ability of algorithm,to confirm measurement errors,improve the accuracy judgment ability for electricity meter measurement errors,and achieve the measurement error detection of smart electricity meter.The test results show that this method has achieved high detection accuracy in multiple seed regions,and overall has good stability and generalization ability.