Electric theft system detection based on genetic algorithm optimization neural network

Electricity theft detection has always been a research problem in the power system. This paper introduces a novel approach utilizing genetic algorithm-optimized neural networks (GA-ANN) for electricity theft detection in power systems. The research focuses on leveraging raw data from current and voltage measurements to train and optimize neural network parameters, enhancing theft detection robustness. To validate the proposed approach, four simulated power system mechanisms are employed, showcasing the GA-ANN model’s effectiveness in accurately identifying theft incidents across diverse scenarios. Experimental results demonstrate the model’s high accuracy and robustness in theft detection, surpassing traditional models and algorithms.