Abnormal behaviour analysis algorithm for electricity consumption based on density clustering. Issue 10 (12th June 2019)

• Record Type: Journal Article
• Authors: Xiang, Min; Rao, Huayang; Tan, Tong; Wang, Zaiqian; Ma, Yue
• Is Part Of: Journal of engineering. Volume 2019:Issue 10(2019)
• Page Start: 7250
• Page End: 7255
• Publication Date: 2019-06-12
• Subjects: power consumption -- pattern clustering -- load forecasting -- power engineering computing; similar user electricity consumption model -- historical electricity consumption model -- abnormal electricity consumption behaviour -- abnormal behaviour analysis algorithm -- density clustering -- large‐scale electrical load data -- abnormal electricity consumption analysis method -- similar users -- electricity consumption characteristics -- electricity consumption data sequence -- outlier electricity consumption; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.5123
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : How to effectively detect abnormal electricity consumption behaviour from a large‐scale electrical load data is very important to smart grid. An abnormal electricity consumption analysis method based on density clustering is proposed. First, the similar users located in fix area are clustered in accordance with the electricity consumption characteristics. Then, on the basis of electricity consumption data sequence, the outlier electricity consumption for the similar users are obtained with density clustering. The matching degrees of these outliers can be calculated based on the similar user electricity consumption model and historical electricity consumption model. Finally, according to the threshold value, the abnormal electricity consumption can be discriminated with the comprehensive support degree. The simulation results show that this method can effectively identify the abnormal electricity consumption behaviour.