Comparison between ABC and ACO: Tunning of On-Off MPPT for wind systems

Abstract

In this work, a comparative study between two optimization algorithms for On-Off MPPT (Maximum Power Point Tracking) in wind power systems will be presented. The two optimizers considered in this paper are: the artificial bee colony algorithm (ABC) and the ant colony algorithms (ACO). Both of these optimization techniques are formulated to minimize different performance such as Integral Absolute Error (IAE) to determine optimal PI regulator values. In order to improve the performance and robustness properties of the proposed PI, the two tuning mechanisms are used in the optimization part. The system is modeled and tested under MATLAB/SIMULINK environment. The comparison of the performances, either by the test function or by MPPT, between these two optimizers shows the efficiency and superiority of the ABC-based approach proposed in terms of the qualities of the solution obtained, the speed of convergence, and the simplicity compared to ACO.

Neural network optimization algorithm based non-singular fast terminal sliding-mode control for an uncertain autonomous ground vehicle subjected to disturbances

“…14 An MPC controller tuned by Ant Colony Optimization (ACO) is presented in Kashyap and Parhi. 15 In this context, other similar algorithms [16][17][18] are widely used within the scope of control. With a critical view, most of these algorithms have a common issue that resides in the fact that they themselves require an internal parameters setting.…”