Linguistics

Process (computing)

Engineering

Philosophy

Similarity (geometry)

Support vector machine

Similarity measure

Operating system

Data mining

Computer science

Mechanical engineering

Image (mathematics)

Feature selection

Feature (linguistics)

Artificial intelligence

Metering mode

Decision tree

Measure (data warehouse)

Electricity

Machine learning

Electrical engineering

International Journal of Electrical Power & Energy Systems

The theft of electricity affects power supply quality and safety of grid operation, and non-technical losses (NTL) have become the major reason of unfair power supply and economic losses for power companies. For more effective electricity theft inspection, an electricity theft detection method based on similarity measure and decision tree combined K-Nearest Neighbor and support vector machine (DT-KSVM) is proposed in the paper. Firstly, the condensed feature set is devised based on feature selection strategy, typical power consumption characteristic curves of users are obtained based on kernel fuzzy C-means algorithm (KFCM). Next, to solve the problem of lack of stealing data and realize the reasonable use of advanced metering infrastructure (AMI). One dimensional Wasserstein generative adversarial networks (1D-WGAN) is used to generate more simulated stealing data. Then the numerical and morphological features in the similarity measurement process are comprehensively considered to conduct preliminary detection of NTL. And DT-KSVM is used to perform secondary detection and identify suspicious customers. At last, simulation experiments verify the effectiveness of the proposed method.

Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM