Design of fractional comprehensive learning PSO strategy for optimal power flow problems

Optimal reactive power dispatch (ORPD) is one of the paramount issue for the researchers during the investigation of power system performance in dynamic load scenarios. In this paper, a new nature inspired computing paradigm based on fractional order comprehensive learning particle swarm optimization (FO-CLPSO) is designed and implemented for solving the reactive power dispatch problems. The objective of the study is to improve the power system efficiency by reducing line losses, enhancing bus voltage profiles and reducing the operating cost of the system for different load factors. The decision variables for the fitness evaluation are the tap changer settings, generator bus voltages, fixed capacitors and flexible AC transmission systems (FACTS). The operation, validity and scalability of the FO-CLPSO are tested on standard IEEE 30 bus and IEEE-57 bus systems. The exploitation and exploration for FO-CLPSO are further extended using different fractional orders for minimization problems in ORPD to critically analyze the performance by comparing with several state of art counterpart methodologies. The stability, consistency, robustness and reliability of FO-CLPSO for the solution of ORPD problems is also substantiated through detailed statistical analyses including the development of empirical cumulative distribution functions, probability plots, box plot illustrations and histograms both for precision and complexity metrics.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a new computing paradigm based on fractional order comprehensive learning particle swarm optimization for solving the reactive power dispatch problems in power systems. The method is tested and verified to be stable, effective, and reliable.