【摘要】 为评估数字电能计量系统的运行误差,提出了基于多参量退化模型的误差评估方法。数字化电能计量系统等效为多输入单输出系统,确定系统退化参量及参量退化作用量,从而建立多参量退化模型,并给出误差评估约束条件。在求解退化网络过程中,引入了大数据处理方法。针对数据种类繁多且变化速率不一的特点,采用差分归一化数据预处理方法。针对退化网络不能用初等函数描述的问题,采用前馈神经网络逼近退化特性。实现了在已知参量退化影响量的条件下,根据误差评估约束评估数字化电能计量系统误差。实例分析结果显示,所提方法的评估结果与实际运行状态在短时间内相符合,绝对误差小于0.2%,表明该方法能有效地动态评估数字化电能计量系统的误差。更多还原

【Abstract】 To evaluate operating error of digital electrical energy metering system, an evaluation method based on multi-parameter degradation model is proposed. Digital electrical energy metering system is equivalent to a multi-input single-output system where system degradation parameters and degradation acting parameters are determined, aiming at building multi-parameter degradation model and putting forward error evaluation constraint. Big data analysis methods are introduced in process of solving degradation network. As the data are of multiple categories and data changing rates are variable, pre-processing method of differential normalized data is employed. Additionally, feed-forward neural network is adopted to approximate degradation characteristics, because elementary function is incapable of describing degradation network. Therefore, error of digital electrical energy metering system can be evaluated according to error evaluation constraint, assuming degradation acting parameters pre-determined. Example analysis results show that evaluation results using the proposed method is in accordance with operating state in short time, and absolute error is less than 0.2%, demonstrating that the method can evaluate error of digital electrical energy metering system effectively and dynamically.更多还原