Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis

This paper proposes an evolving ant direction hybrid differential evolution (EADHDE) algorithm for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. The EADHDE employs ant colony search to find a suitable mutation operator for hybrid differential evolution (HDE) where as the ant colony parameters are evolved using genetic algorithm approach. The Newton–Raphson method solves the power flow problem. The feasibility of the proposed approach was tested on IEEE 30-bus system with three different cost characteristics. Several cases were investigated to test and validate the robustness of the proposed method in finding optimal solution. Simulation results demonstrate that the EADHDE provides very remarkable results compared to classical HDE and other methods reported in the literature recently. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.

Research highlights
► Optimal power flow problem is solved by hybrid differential evolution method.
► Ant direction search is applied to find a suitable mutation operator.
► Ant colony parameters are evolved by genetic algorithm method.
► Statistical analysis is carried out on voltage stability indices and voltage profiles of load buses for the global best solutions.