Layout optimization of a wind farm to maximize the power output using enhanced teaching learning based optimization technique

The global warming is a major concern in the present era that arises a need of cleaner production of energy. The wind energy is a major source of contribution for such clean energy demands. The Wind farm layout optimization (WFLO) problem deals with the optimum placement of wind turbines in a wind farm so as to maximize the total power output with minimum cost of energy. The placement of turbines is crucial for a wind farm because the power generation of wind turbine decreases if it is in the wake effect produced by the upstream turbines. So, WFLO problem is a challenging combinatorial optimization problem for which many direct search and local optimization method fails to attend the global optimum solution. The Meta-heuristic method often provides the effective solution for such problems in terms of convergence and the quality of the solution. In this work two, different metaheuristics algorithms are proposed to solve WFLO problem. These algorithms are developed by incorporating changes in the basic Teaching-learning based optimization (TLBO) algorithm. The proposed algorithms eliminate the limitations of basic TLBO algorithm to enhance its exploration and exploitation by incorporating effective search techniques. The implementations of the proposed algorithm are effective to optimize the position of wind turbines in a wind farm to maximize the expected power output of a wind farm with a minimum investment cost. The proposed algorithm is investigated for WFLO problem and a set of 10 challenging real life benchmark problems. The numerical results indicate that the proposed method is an effective technique to solve the WFLO problem compared to its basic algorithm and other state of the art methods. The optimum design of the wind farm results in the economical utilization of the wind resource and leads to clean energy production.