考虑模型病态性的智能电表运行误差分析方法

基于数据分析的低压台区电能表误差分析方法仅仅依赖于计量系统中台区计量数据，具有覆盖率高、快速便捷的优点，但受制于模型病态性而无法保证计算所得电表误差的精度。针对由病态性而导致的模型求解困难问题，文章从数据和求解算法两方面着手提高求解精度。通过设计基于贪心策略的数据优选算法，快速选取有利模型求解的数据；通过改进经典吉洪诺夫正则化算法，抑制解的波动，从而有效解决智能电表运行误差检定算法模型病态。以天津电网实际低压台区用电数据为算例，验证了所提方法能够有效提升智能电表运行误差检定算法的计算准确度，为实际的电能表检定维护工作提供科学参考。

Analysis Method for Operation Error of Smart Meter Considering Ill-Conditioned Model

Error verification of the smart meter in low-voltage station areas is of great significance for maintaining the state of meters and saving operating costs.The error analysis method based on big data only relies on electricity measurement data in the advanced metering system,which is of high coverage,fast and convenient,and avoids the heavy work of on-site verification,thereby saving a lot of manpower and material resources.However,because of the ill-condition of the model,the accuracy of the measurement data cannot be guaranteed.Aiming at this problem,this paper uses data preprocessing and regularization algorithm to effectively solve the ill-posed state of verification model of the smart meter error.Finally,taking the electricity data of an actual low-voltage station area as an example,analysis result show s that the method proposed in this paper can effectively improve the calculation accuracy of operation error verification algorithm of the smart meters,and provide scientific reference for the maintenance work of on-site meter verification.