Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine

Advanced Metering Infrastructure (AMI) is an essential segment of the smart grids that is responsible for gathering, measuring and analyzing the electricity demand . Energy losses in the electricity distribution and transmission network and electricity theft detection are major challenges of electricity suppliers around the world. The analysis of consumption data related to the customers is one of the essential resources to identify electricity thieves. In this paper, the Crow Search Algorithm (CSA) is improved and the factors weight ( 푤 ) a nd awareness probability ( 퐴푃 ) are obtained dynamically and used to adjust the parameters 퐶 and 훾 related to the Support Vector Machine (SVM). The results illustrate that the ICSA - SVM framework has acceptable performance and detects fraudulent customers with high accuracy.