A new swarm intelligence optimization approach to solve power flow optimization problem incorporating conflicting and fuel cost based objective functions
Luke Jebaraj, Sithankathan Sakthivel
Abstract
Optimization of Power Flow (OPF) is a notable key tool pertinent to power system process, in both setting up and working phases and it is structured for a specific objective to optimize over power system variables, based on definite constraints. A new method of optimization based on swarm intelligence named Sparrow Search Algorithm (SSA), is proposed in this article to resolve the OPF problem in a most efficient way. The different equipped constraints like power balance, generator capacity, bus voltage limit and line flow, were taken into account. Shunt reactive power compensating elements and tap changing transformer settings were also incorporated in the problem formulation as control variables. The proposed SSA based OPF was constructed by several constraints, formulations and objective functions, scrutinized with higher number of cases (33 cases), for the first time, on the three well-liked IEEE networks (IEEE 30-, 57- and 118-bus) via single and weighted amount multi-objectives. The simulation result was examined and the performance and preeminence of the obtainable method was evaluated against other well-constructed recent optimization studies, reported in the literature. The percentage reduction of fuel rate, active transmission loss and deviation in voltage, were examined and compared with some most important recent studies, specified in the literature. Percentage improvement of voltage stability was also evaluated with recent studies, accounted in the literature.