Adaptive Calculation Method of Line Loss of Distribution Network Based on Genetic Algorithm and Artificial Neural Network

ABSTRACT Traditional methods for calculating distribution line losses often rely on complex mathematical models and extensive operational data, leading to cumbersome processes that struggle to ensure accuracy and timeliness in practical applications. To address these limitations, this paper proposes an adaptive calculation method for distribution line losses based on a genetic algorithm (GA) and an artificial neural network (ANN). The approach integrates automated data preprocessing using MATLAB's mapminmax function for feature normalization, an improved k ‐means clustering algorithm for data grouping, and a genetic algorithm to optimize the initial weights and thresholds of a backpropagation (BP) neural network. A 16‐4‐1 network structure is constructed, and a time‐division calculation model is introduced to enable simultaneous computation across multiple lines. Experimental results demonstrate that the proposed method significantly reduces the mean squared error and enhances the accuracy and efficiency of line loss calculation. The hybrid GA‐BP model combines global search capability with nonlinear fitting, enhances data preprocessing and clustering techniques, and improves data quality and model generalization ability.