Load Flow Studies Using Intelligent Techniques: Review

Abstract

Load flow studies are a vital tool for investigating best-operating conditions and proper future planning of power system network. All power system analysis calculations include load flow studies, which are probably the most significant and common. The load flow analysis is used to determine the magnitude and phase angle of the voltage at each bus, as well as the amount of real and reactive power flowing through each transmission system line. In order to solve non-linear load flow problems considering different constraints, several conventional and intelligent techniques have been developed. While conventional methods usually find a solution in a decent amount of time, they frequently involve numerical robustness issues, such as a narrow convergence region and an ill-conditioned system. The load flow analysis methods based on intelligent techniques do not rely on the starting values of the variables and perform better than conventional methods when the power system becomes highly stressed. This paper provides a comprehensive review of various intelligent techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Fuzzy Logic (FL), Ant Colony Optimization (ACO), Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and others, that are used under various defined conditions for load flow studies of various power system networks.