A Review: Traditional and AI-Based Load Flow Solution Algorithms

A diagnostic report or assessment must be carried out to discover the real status of the power system under inquiry before designing, expanding, and analyzing it. This diagnostic report is called power or load flow analysis, and it is the bedrock for all power system analysis. In this research, an extensive and elaborate review is being conducted on various traditional and modern load flow solution algorithms with the aim of evaluating their strengths and weaknesses for an informed research gap consideration. This study considered recent (last five years) research conducted using traditional load flow solution algorithms like Newton Raphson (NR), Gauss Siedel (GS), and fast decoupled (FD) with modern artificial intelligence based LF algorithms to discover recent trends for knowledge advancement and power system stability and reliability. According to the research, NR is the most effective of the numerous conventionally employed LF solution algorithms because it converges more quickly than GS and FD do in the face of network complexity. Also, the bulk of modern AI-based LF solution algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) tend to be computationally quick with lower accuracy. To strike a balance between speed and accuracy, future research should focus on comparative reviews of various AI algorithms for the best LF solution.