Marine predators algorithm for solving single-objective optimal power flow.

Area of Science:

• Electrical Engineering
• Computational Intelligence
• Optimization Techniques

Background:

• The optimal power flow (OPF) problem is crucial for efficient power system operation, planning, and management.
• Existing methods for OPF face challenges in balancing multiple objectives and ensuring system stability.

Purpose of the Study:

• To introduce and evaluate the Marine Predator Algorithm (MPA) for solving single-objective OPF problems.
• To assess MPA's ability to minimize fuel cost, real and reactive power loss, voltage deviation, and enhance voltage stability.

Main Methods:

• The study employs the Marine Predator Algorithm (MPA), a metaheuristic optimization technique inspired by marine predator foraging strategies.
• MPA utilizes Levy walk and Brownian movements for widespread foraging and an optimal encounter policy for predator-prey interactions.

Main Results:

• MPA was applied to the single-objective OPF problem, considering various performance metrics.
• Testing on the IEEE 30-bus system showed MPA's effectiveness in optimizing power flow parameters.

Conclusions:

• The Marine Predator Algorithm (MPA) is a competitive and effective nature-inspired method for solving the optimal power flow problem.
• MPA demonstrates strong performance compared to existing optimization techniques in power system applications.