电力量测数据缺失补齐方法研究与实践

针对电力系统中出现的电力量测数据缺失的问题，本文采用统计方法、插值方法和机器学习方法进行了研究和实践。首先，本文分析了电力量测数据缺失的原因，重点探讨了量测数据在采集、传输、存储以及其他环节对数据缺失的影响。接着，本文详细介绍和分析了三种量测数据缺失补齐方法，并对不同方法进行了实验评估，包括相关系数评价、拟合优度评价和平均绝对误差占比评价等多种评价方法。实验结果表明，机器学习方法在量测数据缺失补齐精度和效果方面优于其他两种方法，表现出更好的效果。最后，本文对研究结果进行了总结和展望，指出机器学习方法在电力量测数据缺失补齐中的应用前景，本文的研究成果可为电力系统中量测数据缺失处理提供一定的参考价值。

Research and Practice on Power Measurement Data Missing Value Imputation Methods

In response to the prevalent issue of missing power measurement data in electric power systems, this study conducted research and practical exploration using statistical methods, interpolation methods, and machine learning methods. Firstly, the paper analyzed the reasons for the missing power measurement data, focusing on the impact of data acquisition, transmission, storage, and other processes on data loss. Subsequently, three methods for data imputation in the presence of missing values were introduced and analyzed in detail, followed by experimental evaluations using various metrics such as correlation coefficient, goodness of fit, and proportion of average absolute error. The experimental results demonstrated that machine learning methods outperformed the other two methods in terms of accuracy and effectiveness in data imputation for missing values. Lastly, the paper summarized the research findings and provided an outlook on the application prospects of machine learning methods in the context of power measurement data imputation. The research outcomes presented in this paper can provide valuable insights for handling missing measurement data in power systems and hold relevance for addressing missing data issues in other domains.