Abstract
We proposed a data mining-based method for precise positioning method of users associated with abnormal line loss. First, we based on the gap statistical algorithm (GSA) to determine the optimal number of clusters, using improved dichotomous K-means++ clustering to construct the line loss standard library and anomaly library. Secondly, we calculated the Spearman correlation coefficient (SCC) and Discrete Fréchet Distance (DFD) of the power consumption and line loss of each user during the abnormal time period. Based on the joint research of SCC and DFD, we constructed a new comprehensive evaluation index. By using TOPSIS algorithm, the descending order of the index value help us realize the precise positioning of all abnormal users. The example uses actual on-spot data in a certain area for verification and analysis, and the results show that the proposed method has better performance in clustering effectiveness, time consumption for calculation and identification accuracy.
Project Supported by Science and Technology Project of State Grid Shandong Electric Power Company “Research on the key technology of intelligent judgment and precise positioning of abnormal line loss rate in the same period based on the fusion of multisource data and Internet of things” (5206091900C7).
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Liang, Z. et al. (2021). Research on Accurate Location of Line Loss Anomaly in Substation Area Based on Data Driven. In: Tian, Y., Ma, T., Khan, M.K. (eds) Big Data and Security. ICBDS 2020. Communications in Computer and Information Science, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-3150-4_33
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DOI: https://doi.org/10.1007/978-981-16-3150-4_33
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