## 1 Introduction  
With the proposal of the strategic goal of \"carbon peaking and carbon neutrality\" in China and the large-scale access of new energy power generation, the traditional power production model has been broken, and the planning, management, and dispatch of power production have become increasingly complex [1]. The data of the power grid presents the characteristics of multisource heterogeneity, scattered data, large scale, rapid change, and many types [2]. The acquisition of distribution data is the basis for the analysis of distribution network operation [3]. The use of a feeder terminal unit (FTU), distribution terminal unit (DTU), and the emerging transformer terminal unit (TTU) in recent years has caused the collection of multistate measurement data in the distribution network to be realized in an orderly manner. At the same time, effective data cleaning of these measurement data can provide high-quality data sources for the realization of multitype protection and fault isolation transparency and online safety checking of the distribution network [4, 5]. Therefore, the cleaning of abnormal data is greatly important in practical engineering applications [1]. Data cleaning is a method used to detect and eliminate errors and inconsistencies in data [6]. The power grid can be simplified into a physical topology structure composed of power generation nodes and power transmission networks. A strong correlation exists between each node and line through complex physical connections. At the same time, due to the inertia of power system equipment and system inertia, the power system operation data have time correlation in a continuous period [7]. At present, the research on the identification and processing of abnormal data in the power system can be divided into three categories according to the temporal and spatial correlation analysis of the measured data. The first category is to clean the data according to the temporal correlation of the measured data. The network filters the sampled signal from the DC system, and the wavelet neural network can organically combine the time-frequency local characteristics of the wavelet transform with the self-learning and adaptive nature of the neural network. Literature [8] proposed a method to convert the detection of similar and repeated records of text data into the detection of similar and repeated records of its binary strings; it provided the daily load data on mutual cleaning for the distribution network, showing the similarity of the daily cycle to ideas. Literature [9] proposed a power grid data cleaning and fusion algorithm based on a time series similarity measure, which uses symbol aggregation, the Euclidean algorithm, and similar sequences to adjust similarity weighting to complete cleaning; it uses a distributed Kalman filtering algorithm to complete data fusion. However, the cleaning algorithm requires a relatively large amount of calculation and is unsuitable for distributed small computing power equipment. The second type is to complete data cleaning according to the spatial correlation of measurement data. Literature [10; 11] proposed a state estimation model based on a deep neural network and a combined particle filter and the convolutional neural network state of the power system. Estimation models demonstrate the spatial correlation of measurement data. Literature [12] used the correlation between fault remote signaling data to group remote signaling displacement data, and then transform the fault diagnosis problem of remote signaling displacement data into the classification problem of sample data in multidimensional space. Literature [13] proposed a load data repair method based on a collaborative filtering recommendation algorithm, which calculates the load range recommendation degree according to the horizontal correlation of load changes within the distribution network area and realizes rapid data correction for abnormal loads.