A machine learning approach-based power theft detection using GRF optimization

Abstract

Purpose

This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users.

Design/methodology/approach

In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm.

Findings

The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method.

Originality/value

The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems.

Citation

Prakash, A., Shyam Joseph, A., Shanmugasundaram, R. and Ravichandran, C.S. (2023), "A machine learning approach-based power theft detection using GRF optimization", Journal of Engineering, Design and Technology, Vol. 21 No. 5, pp. 1373-1388. https://doi.org/10.1108/JEDT-04-2021-0216