Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

TLDR

Nontechnical losses from electrical theft have long plagued power systems, with large‑scale fraud disrupting the demand‑supply balance. The study aims to develop a precise theft‑detection scheme for complex power networks. The scheme uses a two‑level data processing pipeline that first applies a decision tree to classify consumption patterns, then feeds the output into a support vector machine for refined analysis. The approach accurately detects and locates real‑time theft across transmission and distribution levels, significantly reducing false positives and proving practical for real‑time deployment.

Abstract

Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.