Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System

Konstantinos V Blazakis,George S Stavrakakis,Theodoros N Kapetanakis

doi:10.3390/en13123110

Konstantinos V Blazakis, George S Stavrakakis + Show 1 more

Open Access

https://doi.org/10.3390/en13123110

Copy DOI

Abstract

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.

Highlights

Nowadays, societies are even more dependent on electricity due to the extinction of fossil fuels and revolutionary shifts to electric mobility
Electricity power losses occur naturally during the entire operation of the electrical network grid, but the vast amount of losses is caused by electricity theft mainly in the power distribution network
The adaptive neuro fuzzy inference system (ANFIS) is proposed and applied for first time in power theft detection for local low voltage power distribution network; Thirteen different scenarios possible to occur in the real world are established and presented analytically, in order to justify their importance in the proper operation of the power distribution network and they are used in the simulated and discussed case studies in the following; High success rates in power theft detection for most those realistic power theft scenarios were achieved; The adaptive neuro fuzzy inference system (ANFIS) is proposed, implemented and has achieved great success in classifying residential energy consumption patterns to be legal or illegal

Summary

Introduction

Societies are even more dependent on electricity due to the extinction of fossil fuels and revolutionary shifts to electric mobility. The adaptive neuro fuzzy inference system (ANFIS) is proposed and applied for first time in power theft detection for local low voltage power distribution network; Thirteen different scenarios possible to occur in the real world are established and presented analytically, in order to justify their importance in the proper operation of the power distribution network and they are used in the simulated and discussed case studies in the following; High success rates in power theft detection for most those realistic power theft scenarios were achieved; The adaptive neuro fuzzy inference system (ANFIS) is proposed, implemented and has achieved great success in classifying residential energy consumption patterns to be legal or illegal. The rest of this study is organized as follows: Section 2 (the proposed machine learning model framework) describes in brief the ANFIS algorithm used, presents the adopted power theft scenarios.

The ANFIS Classification Method

Power Theft Scenarios

Electricity Consumption Data Preprocess and ANFIS Configuration

Discussion

Confusion matrix andmix mix scenarios

Conclusions