An Association Rules-Based Method for Outliers Cleaning of Measurement Data in the Distribution Network

Hua Kuang,Xian Meng,Risheng Qin,Cheng Guo,Mi He,Ruimin Duan,Xin He

doi:10.3389/fenrg.2021.730058

Abstract

For any power system, the reliability of measurement data is essential in operation, management and also in planning. However, it is inevitable that the measurement data are prone to outliers, which may impact the results of data-based applications. In order to improve the data quality, the outliers cleaning method for measurement data in the distribution network is studied in this paper. The method is based on a set of association rules (AR) that are automatically generated form historical measurement data. First, the association rules are mining in conjunction with the density-based spatial clustering of application with noise (DBSCAN), k-means and Apriori technique to detect outliers. Then, for the outliers repairing process after outliers detection, the proposed method uses a distance-based model to calculate the repairing cost of outliers, which describes the similarity between outlier and normal data. Besides, the Mahalanobis distance is employed in the repairing cost function to reduce the errors, which could implement precise outliers cleaning of measurement data in the distribution network. The test results for the simulated datasets with artificial errors verify that the superiority of the proposed outliers cleaning method for outliers detection and repairing.

Highlights

With the evolution of smart grids, the intelligent monitoring equipment and system are becoming an integral component of the distribution network, collecting a substantial volume of data in order to manage the status and provide timely updates in the network (Alimardani et al, 2015; Wang et al, 2018)
We presents an outliers cleaning method based on association rules, which could found the implicit relationship between features from the historical measurement data and pick up the valuable information on outliers detection and repairing
We developed a association rules-based method for outliers cleaning

Summary

INTRODUCTION

With the evolution of smart grids, the intelligent monitoring equipment and system are becoming an integral component of the distribution network, collecting a substantial volume of data in order to manage the status and provide timely updates in the network (Alimardani et al, 2015; Wang et al, 2018). We adopt the density-based spatial clustering of application with noise (DBSCAN), k-means and Apriori technique to generate the association rules. We presents an outliers cleaning method based on association rules, which could found the implicit relationship between features from the historical measurement data and pick up the valuable information on outliers detection and repairing. The DBSCAN, K-means and Apriori algorithm are chosen for generating the association rules from historical data, which make the detector more flexible and accurate. Since historical measurement data from SCADAS is required processing, using this information, the proposed model generates a list of association rules to evaluate the correlation between. The outliers detection of each feature is based on comparisons between the new real-time observations and the association rules generated from all historical measurement data. {Current [0.2408, 0.2521), Active Power [38.5260, 39.9682), Reactive Power [46.6837, 48.0561)} {Voltage [144.0914, 149.0609)} 0.6957

Method

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT